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Ali Firdaus
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Dewan Editor Editor-in-chief Ali Firdaus, Teknik Komputer, Politeknik Negeri Sriwijaya, Indonesia Managing Editor M. Miftakul Amin Section Editors Slamet Widodo, (Scopus ID=57202841454, Scopus H-Index=2), Teknik Komputer, Politeknik Negeri Sriwijaya, Palembang Adi Sutrisman, (Scopus ID=57201578740, Scopus H-Index=2), Teknik Komputer, Politeknik Negeri Sriwijaya, Palembang Ervi Cofriyanti, (Scopus ID=55848102300, Scopus H-Index=2), Teknik Komputer, Politeknik Negeri Sriwijaya, Palembang, Indonesia Ikhthison Mekongga, (Scopus ID=57194067236; Scopus h-index=1), Teknik Komputer, Politeknik Negeri Sriwijaya, Palembang, Indonesia Yulian Mirza, Teknik Komputer, Politeknik Negeri Sriwijaya, Indonesia Ema Laila, Teknik Komputer, Politeknik Negeri Sriwijaya, Indonesia Mustaziri ,, Teknik Komputer, Politeknik Negeri Sriwijaya, Indonesia Isnainy Azro, Teknik Komputer, Politeknik Negeri Sriwijaya, Indonesia Peer-Reviewers (Mitra Bestari) Muhammad Rahmat Widyanto, (Scopus ID=8921529200, Scopus H-index=6)Ilmu Komputer, Universitas Indonesia, Indonesia Admi Syarif, (Scopus ID=6508046463, Scopus H-index=6), Universitas Lampung, Lampung Andino Maseleno, (Scopus ID=55354910900, Scopus H-index=6), STMIK Pringsewu, Lampung Edy Winarno, (Scopus ID=56049606100, Scopus H-index=3), Universitas STIKUBANK, Semarang Joko Triloka, (Scopus ID=56401829500, Scopus H-index=2), IIB Darmajaya, Lampung Thamrin Latief, Universitas Sriwijaya, Indonesia Herlambang Saputra, Teknik Komputer, Politeknik Negeri Sriwijaya
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Jupiter
Published by Politeknik Negeri Sriwijaya
ISSN : 20852029     EISSN : 2622609X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science
Tentang Jurnal Ini Fokus dan Ruang Lingkup Bidang kajian yang dapat dimuat pada jurnal Jupiter meliputi dan tidak terbatas pada: Mobile Computing Image Processing Computer Graphic Artificial Intelligence Information Retrieval Computer Vision Algorithm & Complexity Data Mining Information System Distributed System Computer & Network Security Cloud Computing Cryptography Human Computer Interaction Speech Processing Natural Language Processing Pervasive Computing Social Media Semantic Web Technologies Game & Multimedia Proses Peer Review Artikel yang masuk ke dalam Jurnal JUPITER akan dilakukan review oleh editor dalam gaya selingkungnya dalam waktu sekitar maksimal 1 minggu. Dari proses review format maka akan dilanjutkan dengan proses review oleh tim reviewer Jurnal JUPITER. Dalam hal ini membutuhkan waktu sekitar 1 minggu - 2 bulan. Proses review artikel menggunakan Double Peer Review. Jika artikel dinyatakan Diterima, maka penulis tidak perlu untuk melakukan revisi. Namun jika artikel dinyatakan Diterima dengan perbaikan, penulis harus memperbaiki artikel sesuai dengan hasil review dan mengembalikan ke tim redaksi melalui OJS Jurnal JUPITER dalam kurun waktu 1 - 2 minggu. Sejarah Jurnal Jurnal Jupiter merupakan jurnal yang diterbitkan oleh Jurusan Teknik Komputer Politeknik Negeri Sriwijaya. Penerbitan jurnal ini bertujuan sebagai wahana komunikasi dan informasi ilmiah dalam bidang komputer yang diharapkan dapat membantu para dosen, peneliti, mahasiswa, dan para stakeholder untuk mempublikasikan hasil penelitian dan kajian ilmiah yang telah berhasil dilakukan. Sehingga hasilnya dapat dikonsumsi oleh berbagai komunitas ilmiah di seluruh Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 424 Documents
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Evaluasi Performa Random Forest, XGBoost, dan LightGBM dalam Diagnosis Dini Diabetes Mellitus Hendra, Hendra Kurniawan; Asmaul Dwi Akbar; Nicholas Svensons; Yandi Jaya Antonio; Karnila, Sri; Safitri, Egi; Nurjoko, Nurjoko
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

Diabetes mellitus is a long-term condition marked by elevated blood sugar levels, which can lead to serious complications such as heart disease, kidney failure, and vision impairment. Early detection plays a vital role in minimizing these risks and enhancing patients' quality of life. This research focuses on assessing the performance of three machine learning algorithms—Random Forest, XGBoost, and LightGBM—in predicting diabetes risk. The dataset utilized originates from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), comprising 768 samples with 9 key features. The research methodology involves multiple stages, including data collection, preprocessing, addressing data imbalance using SMOTE, data splitting for training and testing, algorithm implementation, and model evaluation through accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. Findings reveal that Random Forest delivers the highest performance with an AUC score of 86%, followed by XGBoost (83%) and LightGBM (82%). With its strong accuracy, this model holds potential as a valuable tool for early diabetes diagnosis, contributing to faster and more precise medical decision-making.
Penerapan Metode Klasifikasi Decision Tree dalam Prediksi Kanker Payudara Menggunakan Algoritma C4.5 Nurjoko, Nurjoko; Hendra, Hendra Kurniawan; Cici Cahyati; Elvira Uthia Rustanti; Hiya Cahya Mujahidah; Amanda Putri Maharani; Rosita; Agus Rahardi
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

Breast cancer is a deadly disease that require early detection and accurate prediction to improve recovery chance. This research aims to predict breast cancer using Data Mining technique with Decision Tree C4.5 algorithm. The dataset includes attributes such as tumor size, estrogen status, progesterone status, Progesterone Status, Survival Month, and status. These attributes were selected based on their clinical relevance and predictive potential in the context of breast cancer. The classification results showed a high level of accuracy with a prediction history of 658 surviving breast cancer patients and a precision class of 91.90%. This study has an accuracy rate of 89,81%. These findings have the potential to be developed int a medical decision support system to assist in more objective and efficient.   Keywords—Breast Cancer, Data Mining, Decision Tree, C4.5, Prediction, Accuracy
Konversi Citra Rgb Ke Grayscale Menggunakan Python Saputri Nst, Intan Widya; Harahap, Sofinah; Baihaqi, Akmal_
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

 Digital image processing is an important part of computer science that is widely used in various fields, such as video, face, and automated systems. However, as a result of the presence of three colour channels (RGB), colour image processing presents a number of significant problems, including high memory usage and computational complexity. In contrast, many feature algorithms rely on image intensity. Conversion to grayscale can speed up the process and save memory as it simplifies the data by reducing three channels to one. Therefore, the purpose of this research is to implement the RGB to grayscale conversion process using the Python programming language and the OpenCV library. The method used includes collecting RGB data and then converting it to grayscale using OpenCV's built-in functions. The results show that the process runs well, simplifies the data structure, and produces a grayscale image that contains important information making it suitable for the final stage of image processing.   Keywords: RGB, Grayscale, Image Processing, Python, OpenCV
Implementasi Sistem Monitoring Berbasis IOT Pada Peternakan Ayam Alfarizi Palembang Wijaya, Hari; Rini, Arsia; Azro, Isnainy
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

Alfarizi Farm is one of the farms in Palembang located on Jalan Jakabaring, Silaberanti Village, Jakabaring District, Palembang City, South Sumatra. The chickens raised include broiler chickens, local chickens, and layer hens. The lack of adequate facilities to handle stress in chickens also poses a challenge in maintaining stable temperatures on the farm. Many farmers still use conventional methods, such as estimating the temperature based on their feelings about the heat inside the coop, resulting in manual temperature and humidity control that is not always accurate. In addition, the manual temperature monitoring and regulation system also consumes a lot of time and effort for farmers. Therefore, it is necessary to build a control system aimed at reading the temperature and humidity inside the barn using DHT22 sensors, then the data will be sent to the ESP32 microcontroller. If the temperature is detected to be too high, the system will automatically activate a fan to cool the barn, and if the temperature is too low, a heating lamp will be turned on. All obtained data will be sent to the IoT platform, namely Blynk, which allows farmers to monitor the barn conditions directly through a web-based or smartphone application. Keywords—Temperature monitoring, Humidity, Alfarizi farming.
Konseptualisasi Awal Framework Literasi Etis KAA untuk Siswa SD: Analisis Perspektif Guru dan Orang Tua di SDN 023 Palembang Adelin, Adelin; Hartati, Eka; Everhard Riwurohi , Jan
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

The rapid development of Coding and Artificial Intelligence (AI) technology has brought new challenges to the world of education, especially related to the importance of instilling ethical literacy from an early age. This study aims to develop an initial framework for AI ethical literacy that is appropriate for elementary school students by analyzing the perspectives of teachers and parents at SDN 023 Palembang. Through an exploratory qualitative approach, this study collected data from in-depth interviews with 5 teachers (3 class teachers and 2 curriculum developers) and 10 parents/guardians of students, as well as a literature study of previous research related to ethical literacy in the use of AI. The research findings reveal several key needs, including: (1) integration of digital ethics materials into the existing curriculum, (2) practical and contextual teacher training, (3) creative learning methods based on stories and games, and (4) active involvement of parents in the learning process. Based on these findings, this study produces a draft framework of 4 pillars that cover aspects of curriculum, teacher training, teaching methods, and collaboration with parents. Although still hypothetical and requiring further validation testing, this framework provides an important foundation for the development of ethical KAA education at the elementary level. The implications of this study are not only relevant for the development of school policies, but also provide theoretical contributions to the discussion on digital literacy for early childhood.   Keywords: framework, artificial intelligence, coding, ethical literacy, elementary education.
Komparasi Penerapan Adaboost Pada K-NN Dan Decision Tree Untuk Prediksi Penyakit Hati Mukaromah, Hafsah; Ratnasari, Ratnasari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

The liver is a vital human organ that plays a crucial role in detoxification, cholesterol regulation, and various metabolic activities within the body. Impairment of liver function can lead to several diseases such as hepatitis, liver cancer, cirrhosis, and other liver-related conditions. In Indonesia, approximately 0.6% of the population is identified as having hepatitis, despite the implementation of the HB 0–4 immunization program by the Ministry of Health. Liver disease is a common public health issue, with WHO data reporting an annual death toll of 1.2 million people due to liver-related illnesses in Southeast Asia and Africa. The importance of early detection of liver disease symptoms highlights the need for a predictive system capable of accurately identifying individuals at risk. This study employs a machine learning approach using K-Nearest Neighbor (K-NN) and Decision Tree classification algorithms, enhanced by the application of the Adaboost ensemble learning technique to optimize their performance. Evaluation results show that Adaboost improves the accuracy of the K-NN algorithm to 95.77% and the accuracy of the Decision Tree to 100%. Although the improvement in K-NN is quite significant, Adaboost does not have a substantial impact on the accuracy of the Decision Tree. This research indicates that the Adaboost method is effective in enhancing the classification performance for liver disease, particularly when applied to the K-NN algorithm.
Etika Digital dan Teknologi Kunci Kewirausahaan dalam Industri 4.0 Sriyeni, Yesi; Effendi, Hendra; Veronica, Maria; Everhard, Jan
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

The Fourth Industrial Revolution has transformed the labor market structure through digitalization, automation, and the integration of technologies such as AI, IoT, and big data. One of the most evident impacts is the emergence of the gig economy—a platform-based work model that offers high flexibility but is often accompanied by income uncertainty, limited job security, and minimal legal protection. These conditions give rise to serious ethical dilemmas, particularly regarding the power imbalance between workers and platform companies. This study aims to analyze the application of digital ethics principles in entrepreneurial practices within the gig economy and to identify the emerging ethical challenges. The method used is a literature review focusing on digital entrepreneurship, gig economy characteristics, and the principles of business ethics and algorithmic ethics. The results indicate that fairness, honesty, responsibility, and transparency are fundamental to ethical entrepreneurship in the digital context. Algorithmic transparency, fair compensation, human-centered management approaches, and strong regulatory interventions are essential to ensure worker well-being and protection. These findings highlight the crucial role of digital ethics as a balancing force in building an inclusive and sustainable platform-based work ecosystem.   Keywords— Digital Ethics, Industry 4.0, Gig Economy, Enterpreneurial Ethics
Sistem Monitoring Kontrol Kecepatan Motor 3 Phase Berbasis Internet Of Thing (IoT) Volta, Yonki Alexander; Nofiansah; Herman Yani; Fadil Asnani; Carolina, Yuni; Hidayat, Risqy
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16946200

Abstract

Three-phase induction motors are widely used in industry due to their reliability and efficiency. However, manual speed control systems limit flexibility and operational efficiency. This study aims to design a three-phase motor speed monitoring and control system based on the Internet of Things (IoT) by integrating an ESP32 microcontroller and a Variable Speed Drive (VSD). The system is controlled in real time using two applications: Blynk for control and Smart Life for power monitoring. Testing was carried out by varying the frequency from 10 Hz to 50 Hz, observing voltage, current, and motor rotational speed (RPM). The results show that an increase in frequency is directly proportional to the increase in motor speed, with a detected current imbalance between phases, indicating the need for additional protection. The system demonstrates precise, responsive control and is feasible for automation in small to medium-scale industries.
Sistem Monitoring Dan Prediksi Konsumsi Listrik Menggunakan Metode Long Short-Term Memory (LSTM) Berbasis Internet Of Things (IOT) Amir Putra, Muhammad Rifqi; Saputra, Herlambang; Ami, Hidayati
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 3 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17119678

Abstract

The development of Internet of Things (IoT) technology enables real-time and efficient measurement and monitoring of electricity consumption. This study aims to design and develop an IoT-based electricity consumption monitoring and control system equipped with a prediction feature using the Long Short-Term Memory (LSTM) algorithm. The system uses the PZEM-004T sensor to measure electrical parameters such as voltage, current, power, and energy, which are then transmitted via the MQTT protocol using an ESP32 microcontroller. Electricity consumption data is displayed on a mobile application and stored in a Supabase database. In addition to monitoring features, the system also provides control over electrical devices through a relay, as well as user-configurable scheduling and consumption limit settings. The electricity consumption prediction feature is developed to provide estimated monthly bills or estimated time until prepaid electricity tokens run out, based on historical data. The implementation results show that the system is capable of performing real-time monitoring and control, as well as providing informative visualizations of consumption history in graphical form. This system is expected to help users manage their power consumption more wisely and efficiently.
Rancang Bangun Smart Box Untuk Penyimpanan Paket Menggunakan Arduino Nano Dan Esp32-Cam Berbasis Internet Of Things Riezqi, Adri Dzaki Fateha; Saputra, Herlambang; Sutrisman, Adi
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 3 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17118468

Abstract

With the increasing volume of online shopping, the need for a secure and reliable package storage system has become more critical. To address issues such as loss, damage, and failed deliveries due to the recipient's absence, a Smart Box system based on the Internet of Things (IoT) was designed using an Arduino Nano microcontroller and an ESP32-CAM module. This system enables automatic locking and unlocking of the box through barcode scanning and is equipped with a camera to document the package reception process. The ESP32-CAM module also sends notifications and images to the package owner via a Telegram bot. Test results show that the system can accurately read barcodes and successfully send photos and notifications through Telegram. It can be concluded that this IoT-based Smart Box is secure and feasible to be implemented as a solution to enhance the safety and autonomous management of package storage.

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