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JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer)
Published by Universitas Merdeka Malang
ISSN : 26565722     EISSN : 2685497X     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics
Sistem Tenaga Generator, Distribusi Daya, Konversi Daya Listrik, Sistem Perlindungan dan Teknologi Bahan elektrik Sinyal, Sistem, dan Elektronik Pemrosesan Sinyal Digital, Pemrosesan Gambar, Sistem Robot, Sistem Kontrol dan Sistem Embeded Sistem komunikasi Telekomunikasi, Komunikasi Nirkabel dan Jaringan Komputer Teknologi Informasi Rekayasa Perangkat Lunak, Penambangan Data Multimedia, Komputasi Bergerak, Komputasi Paralel / Terdistribusi, Inteligensi Buatan, Grafik Komputer dan AR / VR Aplikasi Sains Instrumentasi, Matematika, Fisika, Teknologi Geologi, Kimia, Pendidikan Sains dan Teknologi Kesehatan atau Biomedis.
Arjuna Subject : -
Articles 14 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): Desember 2025" : 14 Documents clear
Pemanfaatan Multi-Layer Perceptron (MLP) untuk Deteksi Kanker Firmansyah, Fahrul; Sari, Anggraini Puspita; Sugiarto, Sugiarto
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.13438

Abstract

Cancer is one of the deadliest diseases in the world. This is because patients often do not realize the presence of cancer in their bodies, leading to delayed treatment and the cancer becoming aggressive. Early diagnosis of cancer in women is necessary since the majority of cancer patients are women. One of the markers that can be used to diagnose cancer is the anti-Mullerian hormone, accompanied by other indicators such as lifestyle, BMI, and others. Early diagnosis can utilize the Multi-Layer Perceptron (MLP) algorithm, which is currently a rapidly developing technology. By using the MLP algorithm, an accuracy of 84% is achieved on the training data and test data, with a training-to-testing data ratio of 65:35.
Prediksi Gas Karbon Monoksida dengan Jaringan Syaraf Tiruan berbasis Internet of Things Hirzan, Alauddin Maulana; Maulana, Charis; Handayani, Sri
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.14356

Abstract

Carbon monoxide is a dangerous gas that can cause fatal effects in humans if inhaled in large quantities. To detect it, a model has been developed. This study proposes a prediction model using an Artificial Neural Network (ANN) algorithm to predict carbon monoxide. Of the four ANN models evaluated, the ANN-5K model showed the best performance with an accuracy of 80.18%, followed by ANN-6K with an accuracy of 77.13%, ANN-4K with 66.44%, and ANN-3K with 53.14%. When compared to linear regression, which only had an accuracy of 57.50%, the ANN-5K model was still superior. Thus, the proposed ANN-5K model proved to be more accurate and had a lower error rate compared to other models. The main contribution of this research is a prototype equipped with an ANN model to predict carbon monoxide gas
Klasifikasi Gerakan Bahasa Isyarat Indonesia (Bisindo) menggunakan Arsitektur Transfer Learning Xception Amelia, Meisya Vira; Wahyu Syaifullah Jauharis Saputra; Kartika Maulida Hindrayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15674

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 98% during testing
Sistem Pemetaan Sinyal Tiga Dimensi Wi-Fi Berbasis Android Hardiyanto, Elviawan Riyani Septima; Sashiomarda, Jihan Aulia; Mayasari, Andia Enggar; Sholihah, Ianatush; Nugroho, Yusuf Sulistyo
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15707

Abstract

The demand for stable Wi-Fi access continues to rise, but changes in room layouts often lead to degraded signal quality, necessitating efficient detection solutions. This research aims to develop an Android-based application for three-dimensional (3D) Wi-Fi signal mapping, leveraging smartphones as affordable devices to identify problematic areas. The software development employed the Agile methodology, with key features including 3D grid clustering, color-based visualization according to signal strength (-30 dBm to -100 dBm), and multi-SSID support. Evaluation was conducted in two stages: functional testing using the Black Box Testing method, and usability testing using the System Usability Scale (SUS) instrument. Evaluation results showed a usability score of 77.22 (categorized as "Good"), with 44.4% of users willing to recommend the application. This application proved effective for rapid mapping in various environments, particularly in multi-story buildings and for incidental events, offering advantages such as low cost, an intuitive interface, and comprehensive spatial visualization.
Sistem Cerdas untuk Menganalisis Parameter Pengisian Cepat Baterai Kendaraan Listrik menggunakan Raspberry PI 4 Halomoan, Advent Samuel; Amperawan , Amperawan; Rasyad , Sabilal
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15708

Abstract

Advances in electric vehicle (EV) technology drive the need for fast and efficient battery charging. However, fast charging can cause problems such as overheating, cell degradation, and decreased battery performance. This research develops a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)-based intelligent system running on Raspberry Pi to monitor and predict battery charging parameters in real-time. The system processes data from voltage, current, power, temperature, and State of Charge (SOC) sensors to detect critical conditions such as overcharging and overheating. Equipped with a Human-Machine Interface (HMI) for live data visualization, the system is able to predict SOC with high accuracy (MAE 1.97%, RMSE 2.84%) and respond to automatic control in less than 2 seconds. This integration improves the efficiency and safety of EV battery fast charging.
Software Quality Analysis using the ISO 25010 Standard on the Digital Information System of the Blitar City Government Widiastuti, Diajeng Putri; Nur Cahyo Wibowo; Eka Dyar Wahyuni
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15884

Abstract

The Online and Integrated Personnel Information System (SIKOI) was developed by the Regional Civil Service and Human Resource Development Agency (BKPSDM) to manage the administrative data of approximately 3,200 employees in the City of Blitar. As the system is still under development, this study aims to evaluate the quality of the SIKOI application using the ISO/IEC 25010 standard, which covers both functional and non-functional aspects. The research methods include direct observation and literature review regarding software quality standards. The evaluation was conducted across seven key characteristics: functional suitability, performance efficiency, compatibility, usability, reliability, security, and portability. The results of this study provide insights into the system’s quality and assist system managers in formulating appropriate improvement measures. SIKOI is considered sufficiently feasible to support personnel management in Blitar City; however, several aspects still require improvement in order to fully optimize its overall performance.
Dashboard Berbasis Web untuk Pemantauan Status Gizi Anak: Klasifikasi Otomatis Z-Score WHO, Visualisasi Longitudinal, dan Evaluasi Kegunaan Gustaman, Rian Arie; Rahayu, Andri Ulus; Taufiqurrahman, Imam; Sanaz, Fittur Farabi
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16079

Abstract

This study addressed the need for timely monitoring of child nutritional status by designing, implementing, and evaluating a web-based dashboard. The system captured demographic and anthropometric records, automated status classification using World Health Organization z-scores, and visualized longitudinal growth trajectories. Development followed an iterative, user-centered process and was validated with synthetic records and formative testing by community health workers. Functional testing confirmed correct operation of data entry, classification, and reporting. Against reference calculations, category assignments matched all twenty test cases. Performance profiling indicated responsive interaction (time to first byte about 180 milliseconds; time to interactive about 1.3 seconds), and usability measured with the System Usability Scale fell in the “good” range. Findings suggested readiness for community use and a scalable foundation for future integration and field deployment.
Pengenalan Kualitas Tempe Berbasis YOLOv8 untuk Deteksi Dini Kegagalan Fermentasi Mahfudi, Isa; Kusumawardania, Mila; Moechammad Sarosa, Moechammad; Setiadi, Chandrasena; Riatma, Galih Putra; Soelistianto, Farida Arinie; Muslimah, Nabila Izzatul; Izati, Nadia Yumni
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16091

Abstract

Tempe is a traditional Indonesian food whose fermentation process is highly influenced by temperature, humidity, and soybean quality. Inadequate environmental conditions can lead to fermentation failure, reducing product quality and causing economic losses. Traditionally, quality assessment of tempe has been carried out manually by artisans, which is subjective and inconsistent. This study aims to develop an automatic tempe quality recognition system based on YOLOv8, implemented on a Raspberry Pi 4B with a Logitech C270 camera, monitoring webserver, and buzzer as an early warning system. The YOLOv8 model was trained to detect two main categories, namely good tempe and failed tempe, through real-time visual analysis. Experimental results show system performance with an average accuracy of 93.1%, precision of 93.5%, recall of 91.2%, and mAP@50 of 94.7%. Confidence score analysis indicates that the model is more certain in detecting failed tempe (0.94–0.95) compared to good tempe (0.80–0.86), due to clearer visual differences.
Hybrid Sampling untuk Meningkatkan Akurasi Deteksi Kanker Serviks pada Data Tidak Seimbang: Kajian Komparatif Widodo, Slamet; Samudi; Brawijaya, Herlambang
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16134

Abstract

Cervical Cancer has a high mortality rate among women, driving the adoption of early detection systems based on machine learning. However, their implementation is hindered by class imbalance issues, as seen in the UCI Cervical Cancer Behavior Risk Dataset, where positive cases constitute only 5.8–7.3% of the data. This study proposes an evaluation of resampling techniques—including SMOTE, ADASYN, Random Undersampling, and Borderline-SMOTE—combined with classification algorithms such as RF, XGBoost, LR, GNB, and k-NN. Using Stratified K-Fold Cross Validation to preserve the original class distribution in each fold and ensuring resampling is applied only to the training data in each iteration, the results demonstrate that Borderline-SMOTE significantly improved model performance. Specifically, the Random Forest model achieved a Recall of 0.87 and an AUC-ROC of 0.94. These findings are expected to provide a foundation for future research focused on optimizing adaptive sampling methods
Analisis Daya Keluaran PLTS–Turbin Angin terhadap Suhu Kotak Pendingin Portable Terintegrasi GPS IoT: Indonesia Baihaqi, Mas Ahmad; Abdillah, Hartawan Abdillah; Adi Mulyadi; Asrori, Tamam Asrori; Muhammad, Alief; Fauzan, Ahmad; Afandi, M. Bayu
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16183

Abstract

The main challenge faced by small-scale fishermen in Probolinggo is the deterioration of fish catches due to limited cooling systems. This study developed a portable cooling box powered by a hybrid solar–wind energy system integrated with GPS–IoT. The system was designed using a 100 Wp solar panel, a 450 W wind turbine, a 100 Ah battery, a 3000 W inverter, and temperature sensors with IoT communication modules for real-time monitoring. Experiments were conducted for 12 hours under three operating modes: solar, wind only, and hybrid. The results show the hybrid mode maintained the cooling box temperature below 8 °C for more than 12 hours, while the solar-only and wind-only modes lasted only 4–7 hours. The GPS–IoT integration enabled simultaneous monitoring of vessel location and energy conditions. Therefore, the proposed system proved to be more reliable in preserving fish quality while enhancing efficiency and safety of small-scale fishermen through renewable energy.

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