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Radiyan Rahim
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jsit@rcf-indonesia.org
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Jl. Garuda III Blok C/10 Komplek Pondok Permai, Kel. Limau Manis Salatan, Kec. Pauh, Kota Padang, Provinsi Sumatera Barat.
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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 188 Documents
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Implementation Of Dashboard-Based Business Intelligence And Forecasting For Hiv/Aids Case Trend Analysis At Dr. M. Djamil Padang Hospital: A Case Study In The Period 2020–2025 To Improve The Quality Of Health Services And Data-Driven Prevention Program Planning Firdaus; Marfalino, Hari; Wahyuni, Ritna
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.566

Abstract

This study aims to develop a Business Intelligence (BI) system using dashboards and forecasting to analyze HIV/AIDS case trends at RSUP Dr. M. Djamil Padang from 2015 to 2025. Given the increasing number of HIV/AIDS cases, a data-driven approach is essential for effective planning and decision-making. The methodology includes collecting historical HIV/AIDS case data, performing data cleaning and transformation (ETL), and constructing an interactive dashboard using BI platforms such as Tableau or Power BI. Additionally, statistical forecasting models are applied to predict future case trends. The results indicate that the developed BI dashboard effectively presents informative data visualizations, facilitates trend identification, and supports the planning of HIV/AIDS prevention and intervention programs. The forecasting models provide accurate predictions, aiding in resource allocation and evidence-based policy planning. In conclusion, implementing a BI system with dashboards and forecasting at RSUP Dr. M. Djamil Padang enhances the efficiency of monitoring and managing HIV/AIDS cases, thereby supporting more targeted decision-making in disease prevention and control efforts.
Analisis Pola Kegiatan Belajar Mahasiswa Terhadap Keberhasilan Akademik Menggunakan Algoritma Apriori Saputra, Helmi; Uneputty, Rivaldo Aldino; Simyapen, Leonardo Agustinus; Mustamir, Mohammad Farhan Binalawan; Ikawanti, Fellisia Ayu; kusumawati, Surya putri
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.567

Abstract

Permasalahan dalam penelitian ini adalah belum optimalnya pemanfaatan data perilaku belajar mahasiswa untuk memahami faktor-faktor yang memengaruhi keberhasilan akademik. Penelitian ini menawarkan pendekatan baru dengan menerapkan algoritma Apriori untuk mengidentifikasi pola belajar yang berasosiasi dengan IPK tinggi. Metode yang digunakan mengikuti tahapan Knowledge Discovery in Databases (KDD) dan melibatkan 408 mahasiswa dari berbagai perguruan tinggi di Kabupaten Manokwari. Temuan utama menunjukkan bahwa kombinasi motivasi akademik yang tinggi, kehadiran ≥90%, manajemen waktu yang baik, dan pembelajaran daring secara konsisten terkait dengan keberhasilan akademik. Sebanyak delapan aturan asosiasi paling menonjol berhasil diidentifikasi berdasarkan evaluasi metrik support, confidence, lift, dan Zhang’s metric. Kebaruan dari penelitian ini terletak pada konteks geografis dan sosial yang khas serta pemanfaatan algoritma data mining untuk konteks pendidikan tinggi di wilayah Indonesia Timur. Penelitian ini merekomendasikan penerapan pola belajar berbasis data sebagai strategi peningkatan kualitas akademik mahasiswa di masa depan.
Risk Prediction Of Coronary Heart Disease Using A Decision Tree Algorithm Based On Patient Medical Records Akhiyar, Dinul; Nofriadiman; Rahim, Radiyan; Firdaus
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.568

Abstract

Coronary heart disease (CHD) remains one of the leading causes of death worldwide, often due to late diagnosis and inadequate early detection. Early risk prediction of CHD is crucial to improve patient outcomes and reduce mortality. This study aims to develop a predictive model for assessing the risk of coronary heart disease using a decision tree algorithm, based on structured patient medical records. The dataset used contains various clinical features, including age, gender, cholesterol level, blood pressure, blood sugar, ECG results, and exercise-induced angina. A decision tree classifier was selected for its interpretability, ease of implementation, and effectiveness in handling categorical and numerical data. Data preprocessing steps such as missing value handling, normalization, and feature selection were applied to improve model performance. The model was trained and validated using k-fold cross-validation to ensure reliability. Performance was evaluated based on accuracy, precision, recall, and F1-score. The results demonstrate that the decision tree algorithm achieved satisfactory performance in predicting CHD risk, making it a potentially valuable tool for supporting clinical decision-making. This study highlights the importance of integrating data mining techniques into healthcare to enable timely and accurate risk assessment of life-threatening diseases such as coronary heart disease.
Analisis Sistem Pengambilan Keputusan Terhadap Risiko Keselamatan Kerja Dan Lingkungan Kerja Dalam Bongkar Muat Kapal Pada PT Pelabuhan Indonesia (Persero) Regional 2 Teluk Bayur Dengan Menggunaka Metode Analytical Hierarchy Process (AHP) Wahyudi, Indra; Muhammad, Abulwafa; Ariandi, Vicky
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.569

Abstract

This study aims to analyze the decision-making system related to occupational safety and environmental risks in the loading and unloading activities of ships at PT Pelabuhan Indonesia (Persero) Regional 2 Teluk Bayur. With the increasing complexity and risks faced in port operations, it is essential to apply a systematic method in decision-making. The Analytical Hierarchy Process (AHP) method was chosen as an analytical tool to evaluate various factors affecting safety and environmental work.Through this research, data were collected from loading and unloading activities over the past five years, which included an analysis of potential risks. The results of the study indicate that the application of AHP can assist in identifying and prioritizing risk factors, as well as providing recommendations for more effective hazard control. This research also found that with a good decision-making system, PT Pelabuhan Indonesia (Persero) Regional 2 Teluk Bayur can enhance occupational safety and minimize negative impacts on the environment. From the analysis results, it is hoped that this research can make a significant contribution to the development of risk management systems in the port sector and serve as a reference for other companies in managing occupational safety and the environment.
Representasi Calon Arang Sebagai Simbol Teknologi Dalam Iklan Marjan 2025 Suardi, Melisa; Apriliana
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.570

Abstract

This research examines the transformation of Calon Arang mythology in the Marjan 2025 advertisement, which represents the character as an artificial intelligence (AI) in the futuristic world of JKTerra in 2108. Using Roland Barthes' semiotic approach, the analysis focused on the denotative, connotative, and mythical meanings contained in the visual representation of the ad. The results show that this advertisement successfully combines traditional and modern elements, and conveys social criticism of the dominance of technology in human life. Calon Arang's transformation into an AI reflects contemporary concerns about the rapid development of technology, as well as the importance of maintaining a balance between technological progress and human values. This research contributes to understanding branding strategies that combine local culture and global issues in the Indonesian advertising industry.
Rancang Bangun Website Company Profile Berbasis WordPress CMS Pada PT Samha Catra Nusantara Dewi, Ersya Ramdhania; Putri, Ivon Sandya Sari
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.574

Abstract

PT Samha Catra Nusantara merupakan perusahaan real estate yang berfokus pada pengembangan wilayah Kota Bandung. Di era digital, website menjadi media penting untuk meningkatkan visibilitas, kredibilitas, dan sebagai sarana informasi perusahaan. Proyek ini bertujuan untuk merancang website company profile berbasis CMS WordPress yang informatif dan mudah diakses. Metode pengembangan yang digunakan adalah model waterfall, terdiri dari analisis kebutuhan, desain, pengembangan, implementasi, dan perawatan. Website memuat informasi perusahaan, produk, program dan layanan, FAQ, serta fitur kontak yang terhubung dengan media sosial. Evaluasi dilakukan menggunakan metode System Usability Scale (SUS) untuk menilai keberhasilan dari sisi pengguna. Hasil pengujian menunjukkan skor SUS sebesar 83 yang termasuk kategori “sangat baik”, dengan tingkat kepuasan sebesar 93%. Pemilihan CMS WordPress dinilai tepat karena fleksibel, efisien, dan mudah dikelola. Website ini diharapkan dapat menjadi sarana informasi digital yang efektif serta mendukung strategi komunikasi dan pemasaran perusahaan secara berkelanjutan. Kata kunci: CMS; company profile; real estate; System Usability Scale; website; WordPress.
Penerapan Algoritma C4.5 untuk Memprediksi Profit Perusahaan di PT JMS Batam Hakim, Arif Rahman
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.576

Abstract

The advancement of technology and information has significantly transformed various sectors, especially the business world. With rapid developments, access to fast and accurate data has become easier, eliminating the need for lengthy searches. This evolution has gained the attention of many, particularly companies managing industrial estates, where effective decision-making relies heavily on data analysis for annual business planning. PT JMS Batam, a subsidiary of PT JMS, manages the Tunas industrial area. Established in 2008, it is located at Jl. Raja Isa - Tunas Ruko Industrial Estate Blok 1A No. 10 Batam Center. The company has accumulated a wealth of performance-related data over the years; however, this information has not been effectively utilized. By implementing data mining technology, specifically the C4.5 algorithm, PT JMS Batam aims to process its untapped transaction data. This approach will help uncover valuable insights that can enhance the company's operations. The focus will be on generating predictive knowledge that can aid in forecasting profit achievement, ultimately supporting better strategic planning and decision-making processes within the organization.
Pengembangan Sistem Deteksi Herpes Kulit Menggunakan Metode Thresholding Dan Edge Detection Wiyandra, Yogi; Wahyuni, Suci; Yenila, Firna
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.577

Abstract

Herpes zoster merupakan infeksi virus yang menyerang kulit dan sistem saraf perifer, ditandai dengan kemunculan lesi kulit berwarna kemerahan berbentuk ruam atau lenting. Deteksi dini terhadap lesi herpes penting untuk mendukung diagnosis klinis dan mencegah komplikasi lanjut. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis lesi herpes berbasis segmentasi citra digital. Metode yang digunakan mencakup akuisisi citra pasien, pra-pemrosesan (konversi grayscale dan filtering Gaussian), serta segmentasi area terinfeksi menggunakan metode thresholding Otsu dan deteksi tepi Canny. Hasil segmentasi divisualisasikan dengan pemberian bounding box dan pelabelan numerik terhadap setiap area lesi. Pengujian dilakukan pada citra klinis penderita herpes dengan lima area lesi yang berhasil terdeteksi secara otomatis, masing-masing ditandai sebagai Area 1 hingga Area 5. Luas area tersegmentasi dihitung dalam satuan piksel dan diperoleh hasil sebagai berikut: Area 1 = 2.143 piksel, Area 2 = 1.912 piksel, Area 3 = 3.472 piksel, Area 4 = 1.165 piksel, dan Area 5 = 3.805 piksel. Evaluasi akurasi segmentasi dilakukan dengan perbandingan terhadap ground truth berbasis anotasi manual oleh ahli dermatologi. Hasil evaluasi menunjukkan tingkat akurasi sebesar 99,67%, sensitivitas 91,3%, dan spesifisitas 99,2%. Penelitian ini menunjukkan bahwa metode segmentasi citra digital efektif dalam mengidentifikasi lesi herpes pada kulit dengan tingkat akurasi yang tinggi. Sistem ini berpotensi menjadi alat bantu diagnosis awal yang bersifat non-invasif dan efisien, terutama pada fasilitas layanan kesehatan dengan keterbatasan sumber daya klinis.
Klasifikasi Pasien Penyakit Jantung Di Papua Barat Menggunakan Algoritma Random Forest Nur Rosita, Oktavia; Rahma Mahmud, Niken; Sitinjak, Mangara; Manobi, Yuliana; Sibarani, Natalia
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.581

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian di Indonesia, termasuk di Papua Barat. Perbedaan kondisi sosial, ekonomi, dan akses Kesehatan antara suku asli Papua (OAP) dan non-Papua menimbulkan variasi risiko penyakit jantung. Penelitian ini bertujuan untuk mengklasifikasikan pasien berdasarkan tingkat risiko penyakit jantung menggunakan algoritma Random Forest. Data sebanyak 400 pasien diperoleh dari Rumah Sakit Provinsi Papua Barat, terdiri dari 200 pasien OAP dan 200 pasien non-OAP. Klasifikasi dibagi dalam tiga kategori: Sangat Berisiko, Berisiko, dan Kurang Berisiko, yang ditentukan berdasarkan enam tingkatan penyakit penyerta. Hasil evaluasi menunjukkan bahwa model Random Forest memiliki akurasi tinggi sebesar 99,16%, dengan precision dan recall mencapai 100% untuk kelas Sangat Berisiko dan Kurang Berisiko, serta 98,55% untuk kelas Berisiko. Temuan ini menunjukkan bahwa Random Forest mampu mengklasifikasikan pasien secara efektif dan dapat digunakan sebagai dasar dalam perumusan strategi pencegahan penyakit jantung yang lebih tepat sasaran. Penelitian ini juga memberikan gambaran penting mengenai perbedaan risiko antara suku OAP dan non-OAP, serta mendukung pengembangan kebijakan kesehatan yang lebih inklusif dan berbasis data.
Klasifikasi Kedelai Gmo Dan Non-Gmo Menggunakan Metode Convolutional Neural Network Yogatama, Dhani; Supatman
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.582

Abstract

The rapid advancement of Genetically Modified Organisms (GMO) in agriculture raises concerns regarding food safety, labeling, and consumer protection, especially in soybean commodities. Due to the high visual similarity between GMO and and non-GMO soybeans, traditional identification methods such as molecular testing are often impractical for real-time inspection. This research proposes a classification approach using Convolutional Neural Network (CNN) to automatically distinguish between GMO and non-GMO soybean seeds based on digital images. The dataset used consists of 1,000 soybean seed images, evenly divided between GMO and non-GMO categories, collected using a controlled imaging setup. The preprocessing stage involved cropping, resizing images to 128x128 pixels, and pixel normalization. The dataset was then split into a 70% training set, 10% validation set, and 20% test set to ensure robust model evaluation. The CNN model architecture includes convolutional, pooling, and dense layers, trained using the Adam optimizer and categorical crossentropy loss function. The evaluation results show that the model achieved a test accuracy of 99.00%, with high precision, recall, and F1-score for both classes. These findings demonstrate that CNN can be used to classify soybean seeds without manual feature extraction, offering a practical solution for quality control in agriculture and food processing industries.

Page 13 of 19 | Total Record : 188

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