Elkom : Jurnal Elektronika dan Komputer merupakan Jurnal yang diterbitkan oleh Universitas Sains &amp;amp;amp; Teknologi Komputer (UNIVERSITAS STEKOM). Jurnal ini terbit 2 kali dalam setahun yaitu pada bulan Juli dan Desember. Misi dari Jurnal ELKOM adalah untuk menyebarluaskan, mengembangkan dan menfasilitasi hasil penelitian mengenai Ilmu bidang Teknik Elektro dan Informatika, sebagai media bagi para dosen, guru, peneliti dan para praktisi dalam bidang teknologi informasi dari seluruh Indonesia, dalam melakukan pertukaran informasi tentang hasil-hasil penelitian terbaru yang telah dilakukan.
Analisis Kinerja Metode Long Short-Term Memory (LSTM) dalam Klasifikasi Sentimen Ulasan Pengguna Shopee
User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.
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Rancang Bangun Antena Mikrostrip Patch Triangular Pada Frekuensi 1800 Mhz Sebagai Penguat Jaringan Internet Di Kebun Raya Balikpapan
Penelitian ini bertujuan untuk merancang dan merealisasikan antena mikrostrip patch berbentuk segitiga (triangular) yang bekerja pada frekuensi 1800 MHz, sebagai penguat jaringan internet di kawasan Kebun Raya Balikpapan. Lokasi tersebut diketahui memiliki kualitas sinyal yang kurang baik untuk beberapa provider. Antena yang dirancang menggunakan dua konfigurasi, yaitu MIMO 8x8 dengan teknik pencatuan langsung dan array 8x1, yang kemudian disimulasikan menggunakan CST Studio Suite 2019 dan diuji performansinya menggunakan metode Speedtest. Hasil simulasi menunjukkan bahwa antena 8x8 memiliki nilai VSWR sebesar 1,50, return loss -12,96 dB, dan gain 4,06 dBi dengan pola radiasi omnidirectional. Sementara itu, konfigurasi 8x1 array menunjukkan nilai VSWR 1,24, return loss -19,21 dB, dan gain 6,46 dBi, juga dengan pola radiasi omnidirectional. Hasil pengujian di lapangan dengan tiga kondisi tanpa antena eksternal, antena MIMO dan antena array 8x1 didapatkan hasil bahwa antena array 8x1 memberikan kecepatan unduh tertinggi mencapai 17,227 Mbps, unggah 4,072 Mbps, Jitter 34,9 ms dan packet loss 14,18 % sedangkan antena MIMO 8x8 memberikan kecepatan unduh tertinggi mencapai 13,77 Mbps, unggah 2,462 Mbps, Jitter 49,9 ms dan packet loss 18,43%. Kesimpulannya, kedua jenis antena eksternal yakni MIMO 8x8 dan array 8x1 mampu meningkatkan performa jaringan secara signifikan dan dapat menjadi solusi efektif untuk daerah dengan sinyal lemah. Antena ini diharapkan dapat memberikan kontribusi terhadap akses internet yang lebih stabil di area public yang sangat luas.
Analisis Sentimen X: Kegagalan Timnas ke Piala Dunia 2026 dengan Naive Bayes
The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.
Analisis Sentimen Terhadap Isu Pemblokiran Thrifting Pada Platform TikTok Menggunakan Bidirectional Long Short-Term Memory
The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.
Rancang Bangun Sistem Telemetri Parameter Multi Lokasi Panel Surya Berbasis Komunikasi Jarak Jauh Dan IoT
Penelitian ini bertujuan untuk mengembangkan sistem telemetri guna memantau kinerja panel surya pada beberapa lokasi dengan menggunakan komunikasi jarak jauh dan platform Internet of Things (IoT). Metode pemantauan konvensional memiliki keterbatasan dalam menyediakan data secara real-time pada area yang luas, sehingga evaluasi kinerja jarak jauh menjadi kurang efisien. Untuk mengatasi permasalahan tersebut, dirancang sebuah sistem pemantauan menggunakan mikrokontroler ESP32, sensor INA219 untuk mengukur tegangan dan arus, modul GPS Neo-M8 untuk identifikasi lokasi, modul Real-Time Clock (RTC) DS3231 untuk pencatatan waktu, serta modul LoRa RA-02 sebagai media komunikasi nirkabel. Setiap node pengirim dilengkapi dengan modul MicroSD untuk menyimpan data pengukuran secara lokal. Data hasil pengukuran dikirimkan melalui LoRa ke unit penerima dan ditampilkan secara real-time pada platform Thinger.io. Hasil kalibrasi menunjukkan bahwa sensor INA219 memiliki rata-rata galat pengukuran arus sebesar 0,71% dan galat pengukuran tegangan sebesar 0,1%. Pengujian GPS menunjukkan koordinat lokasi yang stabil dengan tingkat akurasi sekitar ±3 hingga ±8 meter. Seluruh data pengukuran berhasil dikirim, disimpan, dan ditampilkan tanpa kehilangan data yang signifikan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan pemantauan parameter panel surya secara jarak jauh yang andal dan efisien dalam kondisi lapangan.
The Epistemology : Digital Transformation Of Traditional Game Development Based On Machine Learning System
This paper explores the epistemological dimensions of the digital transformation occurring in traditional game development through the integration of machine learning systems. By examining how knowledge creation, validation, and application have evolved in this domain, we identify fundamental shifts in the epistemological frameworks governing game development practices. The research investigates how machine learning has redefined creative processes, technical implementation, and experiential design while challenging traditional notions of authorship, expertise, and knowledge transmission. Through analysis of industry case studies, technological capabilities, and theoretical frameworks, this paper contributes to understanding how machine learning systems are not merely tools but epistemological agents that fundamentally transform how knowledge is generated, validated, and utilized in game development ecosystems.
Analisa Citra Warna Darah Reject Berdasarkan Fitur Histogram Menggunakan KNN
Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.
Analisis Kinerja Sistem Auto Tracking Single Axis Pada Panel Surya 10wp Berbasis Internet Of Things (Iot) Dengan Pemantauan Real-Time
The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.
Classification of Mountain and Beach Tourism Destination Preferences Using Deep Learning Methods
Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.
Prediksi Konsentrasi PM2.5 Resolusi 15 Menit di Kabupaten Brebes Menggunakan Transformer dan GEOS-CF NASA
Fine particulate matter (PM2.5) air pollution poses a serious public health threat in Brebes Regency, Central Java. The main contributing factors are vehicle emissions on the Pantura route, fishing industry activities, and high concentrations during the dry season (June–November). The lack of an accurate sub-hourly forecast model hinders the development of an effective early warning system. This study develops and evaluates a Transformer-based deep learning model to predict PM2.5 concentrations with a 15-minute time resolution. The data used came from NASA GEOS-CF (PM25_RH35_GCC band) accessed through Google Earth Engine using the Python API. The dataset covered the period from 1 January to 22 November 2025, resulting in 7,813 observations per hour, which were then linearly interpolated into 31,249 data points with a resolution of 15 minutes. The Transformer architecture consists of 3 encoder layers, 4 multi-head attention heads, 128 embedding dimensions, 256 feed-forward dimensions, 60 timestep sequence length, and feature augmentation using rolling mean (window = 3) and first difference. Training was performed with TensorFlow-Keras, Adam optimiser, cosine decay scheduler, and Huber loss. Data division was chronological: 70% training, 30% validation. Evaluation on an independent test set (16 August–21 November 2025, 9,357 observations or 97 days 11 hours 15 minutes) resulted in MAE 0.7691 μg/m³, RMSE 1.2052 μg/m³, R² 0.9945, and Explained Variance Score 0.9948. The model is capable of accurately depicting diurnal variations and seasonal anomalies, far surpassing conventional LSTM and GTWR models. This research makes a significant contribution to the field of Information Technology through its satellite big data processing framework for environmental applications,
Effectiveness of Solar-Powered Smart School Yard Lights on Energy Efficiency and Lighting Quality
The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan
Perkembangan kecerdasan buatan (AI) mendorong inovasi dalam analisis keuangan, termasuk prediksi harga saham yang fluktuatif. Penelitian ini bertujuan memprediksi harga saham PT Garudafood Putra Putri Jaya Tbk menggunakan model ARIMA dengan penanganan Outlier sebagai pendekatan awal menuju sistem prediksi yang lebih adaptif. Data harga penutupan harian dari Yahoo Finance dianalisis melalui uji stasioneritas, identifikasi model ARIMA, deteksi Outlier berbasis log-return, serta evaluasi performa menggunakan RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa ARIMA Outlier memberikan performa lebih baik dibandingkan ARIMA dasar. ARIMA standar menghasilkan MAPE 1.32% dan AIC –899.46, sedangkan ARIMA dengan tiga dummy Outlier mencapai MAPE 1.16% dan AIC –900.37. Peramalan 14 hari ke depan menunjukkan pola yang stabil pada kisaran Rp 370–371. Pada data uji, ARIMA dasar memberikan akurasi terbaik pada pertengahan Agustus, sedangkan ARIMA Outlier mencapai akurasi tertinggi pada akhir Agustus dengan prediksi Rp 370.2 yang sangat dekat dengan harga aktual Rp 370.4. Hasil ini menunjukkan bahwa penanganan Outlier meningkatkan ketepatan model, sehingga ARIMA Outlier dapat digunakan sebagai fondasi awal menuju pengembangan sistem prediksi keuangan berbasis AI.
Partially Homomorphic Encryption for Supporting Encrypted Data Retrieval in Relational Databases
Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.
Hybrid Apriori–MST Framework for Revealing the Structural Backbone of Consumer Purchase Patterns
Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.
Klasifikasi Jenis Sampah Organik Dan Anorganik Menggunakan Convutional Neural Network Berbasis Citra Digital
Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.
Klasifikasi Huruf Hijaiyah Berbasis Citra Digital Menggunakan Metode Convolutional Neural Network (CNN)
Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.
Analisis Performansi Pendekatan Machine Learning Pada Deteksi Penyakit Daun Tanaman Kopi
Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.
Perancangan Aplikasi Antrian Pendaftaran Pasien Puskesmas Selindung Berbasis Android
The advancement of information technology has had a significant impact across various sectors, including healthcare. The digitalization of healthcare services has become a solution to improve efficiency, effectiveness, and accessibility for the public. Puskesmas Selindung still uses a manual patient registration system, which leads to several issues such as long queues, extended waiting times, and the risk of lost or damaged patient records. Based on visit data, the number of patients coming to Puskesmas Selindung has increased each year. Therefore, a digital queue system is needed to optimize the patient registration process. This research aims to analyze and design an Android-based patient registration queue application to improve service efficiency at Puskesmas Selindung. The research methods include system requirements analysis, user interface design, and the development of core features to support the online patient registration process. The implementation of this application is expected to reduce long queues, speed up administrative processes, and make it easier for patients to access healthcare services more effectively and accurately. With this Android-based system, the quality of healthcare services at Puskesmas Selindung is expected to improve significantly.
Rancang Bangun Prototype Sistem Monitoring dan Kontrol Suhu dan Kelembaban pada Multi-Kubikel Berbasis IoT
Suhu dan kelembaban merupakan parameter lingkungan yang harus dijaga pada ruang kubikel untuk memastikan peralatan distribusi listrik tetap bekerja secara optimal. Pada multi-kubikel, perbedaan fungsi dan beban menyebabkan karakteristik suhu dan kelembaban pada tiap ruang kubikel tidak sama, sehingga pemantauan secara manual menjadi kurang efektif dan efisien. Penelitian ini bertujuan untuk merancang dan membangun prototype sistem monitoring dan kontrol suhu-kelembaban pada multi-kubikel berbasis Internet of Things (IoT) yang terdiri dari tiga buah kubikel. Sistem ini menggunakan ESP8266 sebagai mikrokontroler utama dan sensor DHT20 sebagai sensor suhu dan kelembaban yang masing-masing dipasang pada kubikel dengan kondisi lingkungan berbeda. Sistem dilengkapi dengan aktuator kipas dan lampu, serta notifikasi real-time melalui LCD dan Telegram. Meskipun kontrol dan monitoring dilakukan secara terpisah pada tiap kubikel, notifikasi kondisi seluruh kubikel terintegrasi pada satu kanal Telegram yang sama. Pengujian kinerja sistem dengan memberikan variasi suhu dan kelembaban yang berbeda untuk tiap kubikel. Kubikel 1 diberi kondisi normal (suhu 35°C-40°C dan kelembaban 50%-70%), kubikel 2 diberi kondisi overheat (suhu di atas 40°C), sedangkan kubikel 3 diberi kondisi overhumidity (kelembaban > 70%). Hasil pengujian menunjukkan sistem mampu melakukan kontrol suhu dan kelembaban dalam ruang multi-kubikel serta mengirimkan notifikasi melalui Telegram dengan tingkat keberhasilan 100% dan rata-rata delay 5,6 detik.
Design and Construction of a Battery Powered Cell Phone Charger and Dynamo Movement
Limited access to electricity sources in remote areas or emergency situations often hampers the availability of power for telecommunications equipment. This research aims to design a hybrid charger that integrates battery chemical energy storage and dynamo mechanical energy conversion as an alternative solution. The research method uses Research and Development (R&D) with a quantitative descriptive approach to test voltage stability and charging effectiveness. The prototype is designed to use an 18650 battery, a 12V dynamo, and a voltage regulator module to stabilize the output. The test results show that the integrated regulator succeeded in stabilizing the dynamo voltage fluctuations (range 5–12V) to a constant voltage of 5V which is safe for cellphones. Effectiveness testing for 15 minutes showed an increase in cellphone battery capacity of 5–10% in battery mode and 2–4% in dynamo mode. It was concluded that although the dynamo mode has lower efficiency due to manual rotation variability, this tool is worthy of being implemented as an emergency power supply solution (emergency kit) that is portable, economical, and has reliable energy redundancy.
Penerapan Metode Design Thinking Dalam Perancangan UI/UX Sistem Pencatatan dan Pemantauan Kekeruhan Air Berbasis Website
PAMTIRTA Tempino is an institution that provides clean water services in the Tempino area. The process of recording water use and monitoring water turbidity is still done manually, making it prone to recording errors and making it difficult to monitor the water quality distributed to the community. This study aims to design a website-based water turbidity recording and monitoring system by focusing on User Interface (UI) and User Experience (UX) aspects using the Design Thinking method. The research follows five stages of Design Thinking: empathize, define, ideate, prototype, and test. Data collection involves observation and in-depth interviews with PAMTIRTA officers. The results include a design with key features such as digital water meter recording, turbidity monitoring dashboards, and complaint services. The prototype was tested using Maze and the System Usability Scale (SUS), achieving a score of 80.1 and falling into the "Good" category (grade B). These results demonstrate that the UI/UX design effectively provides an easy-to-understand, operationally suitable, and efficient solution for PAMTIRTA Tempino's water recording and turbidity monitoring needs. This design offers a ready-to-implement solution to improve the efficiency, accuracy, and quality of clean water services in the Tempino area.
Analisis Hasil Pengukuran Flowmeter Oksigen Bayi Produk Dalam Negeri Dan Produk Luar Negeri Dengan Metode Uncertainty Dan Uji Mann-Whitney
Oxygen is vital therapy where delivery accuracy is crucial, especially for infant patients, to ensure treatment effectiveness and prevent the risks of hypoxia or toxicity. With the implementation of the mandatory Domestic Product Utilization Policy (TKDN+BMP ≥ 40%), evaluating the quality of local products has become an urgent necessity. This study aims to test and analyze the quality and accuracy of domestically produced infant oxygen flowmeters compared to an imported product. The method used was experimental testing, measuring three brands of domestic products and one brand of foreign product at flow rate settings of 0.5, 1, 1.5, and 2 liters per minute (LPM). Each setting point was measured 10 times using a standardized calibrator to ensure data reliability. The measurement results were analyzed to identify the deviation level of each product. The findings of this study are expected to provide an objective conclusion on the quality equivalence of domestic products with imported ones and to identify which product has the lowest deviation rate. This can serve as scientific consideration for hospitals in selecting high-quality infant oxygen flowmeters, thereby supporting the domestic product policy.
Perancangan Knowledge Management System Sebagai Media Berbagi Pengetahuan Mahasiswa Dengan Metode Knowledge Management System Lifecycle
Knowledge must be managed effectively to facilitate transfer between individuals, groups, and organizations. The Informatics Engineering study program currently lacks a system for knowledge management. Currently, the study program facilitates offline discussion forums for the sharing of knowledge gained by lecturers and students. These offline discussion forums require significant costs, time, and space, often resulting in delays in knowledge sharing. This research focused on the analysis and design of a Knowledge Management System to meet the needs of Informatics Engineering students at Universitas PGRI Madiun. The system development method used was the Knowledge Management System Lifecycle (KMSL). In this study, the TIF KMS system using the KMSL method has been successfully built. The results of testing using the Blackbox Testing method showed that 5 scenarios and 18 cases were successfully executed as expected with a 100% success rate. Based on the system test results, the TIF KMS is ready to proceed to the implementation stage. Future implementation can be done by developing additional features such as a digital library.
Implementasi Sistem Informasi Perpustakaan Pada SMK Negeri 3 Oku Selatan Berbasis Desktop
Information technology is currently developing and growing rapidly in all fields, supported by the advancement of computer technology. The application of information systems can also be applied to the financial services sector, including cooperatives like K.S.P Al Hudori. The loan service system remains ineffective due to the manual data management process, which involves writing data into ledgers, which are easily lost or damaged due to the paper-based nature of the data. This research aims to implement a loan information system at K.S.P Al Hudori that can assist with loan data verification, search, and report generation. This information system was designed using Embarcadero XE2 and Microsoft Access 2007 as its database. This system has been implemented at KSP Al Hudori. It is hoped that this information system will simplify the loan management process at K.S.P Al Hudori
Implementasi Sistem Informasi Pinjaman Pada K.S.P Al Hudori Menggunakan Embarcadero XE2
Information technology is currently developing and growing rapidly in all fields, supported by the advancement of computer technology. The application of information systems can also be applied to the financial services sector, including cooperatives like K.S.P Al Hudori. The loan service system remains ineffective due to the manual data management process, which involves writing data into ledgers, which are easily lost or damaged due to the paper-based nature of the data. This research aims to implement a loan information system at K.S.P Al Hudori that can assist with loan data verification, search, and report generation. This information system was designed using Embarcadero XE2 and Microsoft Access 2007 as its database. This system has been implemented at KSP Al Hudori. It is hoped that this information system will simplify the loan management process at K.S.P Al Hudori.
Solar Sonic Repeller : Inovasi Portabel Pengendali Hama Tikus Berbasis Energi Terbarukan
The problems in rice fields are complex and varied, depending on geographic location, rice variety, and growing season. Pests often cause serious economic losses. The Solar Sonic Repeller is an innovative portable pest control device designed to address pest problems by utilizing renewable energy, specifically solar energy. This product aims to offer an environmentally friendly and efficient solution. It works by emitting ultrasonic sound waves with a frequency of 30,000–40,000 Hz. The device's advantages lie in its portability and energy independence, thanks to the use of a charging module powered by an integrated photovoltaic (PV) panel with automatic battery charging during the day. The first test measured the output frequency using an oscilloscope to verify that the oscillator circuit produced waves at the specified frequency. The second test measured the device's effectiveness by examining the pest response to the device at various distances. This test was effective within a maximum radius of approximately 14 m from the center point, covering a rice field area of ​​250 m2.
Analisis Temporal Gerakan Kata BISINDO Menggunakan Landmark Tangan dan LSTM dengan Keluaran Suara Berbasis ESP32 Secara Real-time
Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.
Analisis Sentimen Ulasan E-Commerce Menggunakan Metode SVM
The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.” The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique. Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.
Perancangan Sistem Informasi Penjualan di Toko Oleh - Oleh Berbasis Web
Penelitian ini bertujuan merancang dan mengembangkan Sistem Informasi Penjualan berbasis web untuk Gerai Oleh-Oleh Bali yang selama ini masih menggunakan pencatatan manual dengan buku besar dan perhitungan menggunakan kalkulator. Sistem manual tersebut menyebabkan risiko kesalahan pencatatan, lambatnya proses pencarian data, serta kesulitan dalam pengarsipan data penjualan, pembelian, pemesanan, dan pengelolaan stok barang. Penelitian menggunakan metode pengembangan perangkat lunak Waterfall dengan pendekatan pemodelan Unified Modeling Language (UML). Tahapan yang dilakukan meliputi analisis kebutuhan melalui wawancara dengan pemilik gerai, perancangan sistem (Use Case Diagram dan Class Diagram), implementasi, pengujian unit, pengujian sistem menggunakan metode black-box testing, serta tahap pemeliharaan (maintenance). Sistem yang dibangun mencakup fitur login, dashboard, pengelolaan data master (supplier dan barang), transaksi penjualan dengan dukungan scan barcode, transaksi pembelian, laporan penjualan dan pembelian, serta pengelolaan user. Hasil pengujian black-box menunjukkan seluruh test case berstatus Valid dan sistem berfungsi sesuai harapan. Pada tahap maintenance dilakukan contoh corrective maintenance dengan perbaikan bug pada query laporan penjualan harian.Sistem informasi penjualan berbasis web yang dihasilkan mampu mempercepat proses transaksi, mengurangi kesalahan manusia, meningkatkan akurasi data, serta memudahkan pengelolaan stok dan pembuatan laporan secara real-time. Implementasi sistem ini memberikan solusi efektif bagi Gerai Oleh-Oleh Bali dalam meningkatkan efisiensi operasional dan interaksi dengan pelanggan.
Klasifikasi Jenis Bunga Menggunakan Algoritma Convolutional Neural Network (CNN)
This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.