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Radiyan Rahim
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INDONESIA
Jurnal Sains Informatika Terapan (JSIT)
Published by Riset Sinergi Indonesia
ISSN : -     EISSN : 28281659     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The scope of this journal is all about Computer Science that are: 1. Artificial Intelligence 2. Computer System 3. Data Mining 4. Information System 5. Decision Support System (DSS) etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 286 Documents
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Rancang Bangun Alat Display Suhu Ruangan Menggunakan Sensor LM35 Dan Platform BLYNK Berbasis Internet Of Things Reza Adinova; Supriadi; Dwi Titi Maesaroh
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1175

Abstract

Penelitian ini mengembangkan protype alat monitoring suhu berbasis internet of things (IoT) yang menggunakan sensor LM35 untuk pengukuran suhu lingkungan serta platform Blynk untuk visualisasi data secarae real-time dalam bentuk grafik pada perangkat Android. Sistem ini dirancang untuk memantau suhu secara jarak jauh, menampilkan data historis, dan memberikan notifikasi jika suhu melebihi ambang batas yang ditentukan. Sistem terdiri dari mikrokontroler NodeMCU ESP8266 sebagai pemroses utama, sensor suhu LM35 untuk akuisisi data, dan koneksi Wi-Fi untuk mengirim data ke server Blynk. Data suhu ditampilkan dalam bentuk grafik digital pada aplikasi Blynk yang dapat diakses melalui smarthphone. Pengujian sistem dilakukan dengan membandingkan hasil pembacaan sensor LM35 terhadap thermometer standar pada berbagai kondisi suhu. Sistem ini memanfaatkan sensor LM35 sebagai pendeteksi suhu karena memiliki tingkat akurasi yang baik serta respons yang linear terhadap perubahan suhu. Sebagai pusat pemroresan data dan modul komunikasi Wi-Fi, sistem ini menggunakan mikrokontroler NodeMCU ESP8266. Sistem mampu memantau suhu real-time dengan akurat dan efektif untuk monitoring jarak jauh.
Pengembangan Sistem Klasifikasi Citra Daging Sapi Dan Daging Babi Berbasis Web Menggunakan DENSENET-121 Siti Nurviatika; Ivana Lucia Kharisma; Nugraha
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1176

Abstract

The circulation of beef and pork products that are difficult to distinguish visually can create challenges for consumers, making an automated meat identification system necessary. This study aims to develop an image classification model for beef and pork using the Convolutional Neural Network (CNN) method with the DenseNet-121 architecture and to implement it in a Streamlit-based web application. The dataset used in this study consists of 6,000 images, comprising 3,000 beef images and 3,000 pork images collected from two different dataset sources. The dataset underwent several preprocessing stages, including resizing, contrast enhancement, normalization, and data augmentation, and was subsequently divided into training, validation, and testing sets with a ratio of 70:15:15. The results show that the DenseNet-121 model is capable of classifying beef and pork images with excellent performance. Based on the evaluation using a confusion matrix and classification report, the model achieved an accuracy of 97.89%, with high precision, recall, and F1-score values for both classes. The trained model was then deployed in a web application that allows users to perform classification through image uploads or direct image capture using a camera. Based on these findings, it can be concluded that the DenseNet-121 architecture is capable of classifying beef and pork images with high accuracy and has the potential to be utilized as a practical tool for meat type identification.
Website-Based Ergonomic Sitting Posture Detection Using YOLOV8 Pose Estimation Nurazizah Zahra; Ivana Lucia Kharisma; Somantri
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1177

Abstract

This study aims to develop a web-based ergonomic sitting posture detection system to reduce postural fatigue caused by prolonged computer use. The proposed system uses a deep learning-based pose estimation method to detect body keypoints and calculate the user's posture angles. The dataset used consists of hundreds of images that have been enhanced in quality and quantity through a data augmentation process. The system then classifies sitting postures into ergonomic and non-ergonomic categories. Test results show that the system is able to achieve a high level of accuracy, with the model achieving 98% Precision, 99% Recall, 99% mAP50, and 82% mAP50-95 in detecting and classifying sitting postures. Furthermore, the web-based implementation allows for real-time monitoring. The results of this study indicate that computer vision technology has the potential to be an effective solution to increase awareness of correct sitting posture and help prevent postural fatigue, especially in academic environments.
Analisis Faktor Dominan Prediksi Dropout Mahasiswa Menggunakan Random Forest, XGBoost dan XAI Norma Devi Kurniasari; Trian Basofi Rohman; Ari Widianto; Anis Shobikah; Gaguk Triono
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1183

Abstract

Masalah mahasiswa yang putus kuliah (dropout) menjadi tantangan signifikan bagi perguruan tinggi karena memengaruhi reputasi institusi dan efisiensi pemanfaatan sumber daya kampus. Untuk mengatasi fenomena tersebut, diperlukan sistem prediksi dini guna mengidentifikasi mahasiswa yang berisiko sejak awal perkuliahan. Penelitian ini bertujuan membandingkan kinerja dua algoritma machine learning, yaitu Random Forest dan XGBoost, sekaligus memetakan faktor utama penyebab dropout menggunakan pendekatan Explainable AI (XAI) melalui metode SHAP (SHapley Additive exPlanations). Studi ini menggunakan dataset sekunder dengan total 4.424 record data mahasiswa yang mencakup variabel demografi, sosial-ekonomi, makroekonomi, dan akademik. Proses eksperimen dan pemodelan dilakukan menggunakan lingkungan Google Colab. Hasil pengujian menunjukkan bahwa model Random Forest menghasilkan tingkat akurasi optimal yang lebih tinggi yaitu sebesar 77,40%, dibandingkan model XGBoost yang menghasilkan akurasi sebesar 76,05%. Melalui analisis interpretasi SHAP, penelitian ini menemukan bahwa jumlah mata kuliah yang lulus di semester dua (Curricular units 2nd sem (approved)) dan status kelancaran pembayaran biaya kuliah (Tuition fees up to date) merupakan faktor paling dominan yang memengaruhi keputusan prediksi. Hasil penelitian ini memberikan dasar empiris bagi pengelola perguruan tinggi untuk memprioritaskan kebijakan intervensi pada aspek akademik tahun kedua serta stabilitas finansial mahasiswa sebagai strategi menekan angka putus kuliah.
Analisis Performa Model Embedding BGE Small Dan Minilm-L6 Terhadap Kualitas Retrieval Menggunakan Metrik Ragas Ahmad Ibrahim Maqbul; Anggun Fergina
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1184

Abstract

The application of Large Language Models in the medical domain is often hampered by issues of hallucination and limited up-to-date knowledge. Retrieval-Augmented Generation offers a solution for connecting LLM with factual data, but the quality of RAG output is highly dependent on the accuracy of the information retrieval process. This study aims to analyze the effect of chunk size and embedding model variations on retrieval quality in a medical chatbot system at the Nusa Putra Farmedika General Clinic. The method used is a comparative experiment by testing three chunk size variations (256, 512, and 1024 tokens) and comparing the performance of two embedding models, BGE Small and MiniLM-L6. The evaluation was conducted automatically using the RAGAS framework, focusing on the Context Recall and Context Precision metrics. These findings were implemented into a medical chatbot prototype as a form of functional validation. The results showed an inverse relationship between chunk size and retrieval quality, with a chunk size of 512 tokens producing the best level of information granularity. The BGE Small model proved to be slightly superior to MiniLM-L6 in capturing the semantics of clinical text. The most optimal configuration was achieved by combining the BGE Small model with a chunk size of 512, which produced the highest average score of 0.59, Context Recall of 0.45, and Context Precision of 0.74. This study recommends this configuration as a technical standard for the development of medical chatbot as a foundational step to improve context relevance and mitigate the potential for hallucinations.
Analisis Perbandingan Kinerja Tools Manajemen Proyek (Trello Vs Jira) Menggunakan Metode Pieces Anggun Fergina; Siti Zahra Sifa; Taufik Hidayat; Imam Sanjaya; Aulia Kusuma Wardani
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1186

Abstract

Effective project management highly depends on selecting the appropriate tools to support team productivity. This study focuses on comparing the performance of two popular project management platforms, Trello and Jira, which are widely used by software development teams. The issue addressed in this research concerns the differences in features and levels of flexibility that often make it difficult for organizations to determine the most efficient tool according to their needs. Therefore, this study aims to objectively evaluate the effectiveness of both platforms and provide recommendations for selecting project management tools that best suit organizational requirements. The benefit of this research is to provide information and references for project managers and software development teams in determining the most suitable project management platform. The method used is a comparative analysis based on the PIECES framework (Performance, Information, Economics, Control, Efficiency, and Service) with a quantitative approach through observation and questionnaire distribution to 30 respondents. The collected data were analyzed based on the six PIECES dimensions to measure the performance level of each platform. The results indicate that Trello achieved higher scores in the Performance (4.8), Economics (4.7), and Efficiency (4.6) dimensions, while Jira outperformed Trello in Information (4.9), Control (4.8), and Service (4.6). Overall, Jira obtained a higher average score of 4.35 compared to Trello's 4.16. In conclusion, Jira is more suitable for managing complex projects that require better control and information management, whereas Trello is more appropriate for small- to medium-scale projects that prioritize ease of use and operational efficiency.

Page 29 of 29 | Total Record : 286

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