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INDONESIA
JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Published by STMIK Global Informatika MDP
ISSN : 24074322     EISSN : 25032933     DOI : -
Core Subject : Science,
Computer Science & IT
JATISI bekerja sama dengan IndoCEISS dalam pengelolaannya. IndoCEISS merupakan wadah bagi para ilmuwan, praktisi, pendidik, dan penggemar dalam bidang komputer, elektronika, dan instrumentasi yang menaruh minat untuk memajukan bidang tersebut di Indonesia. JATISI diterbitkan 2 kali dalam setahun (September dan Maret), makalah yang diterbitkan JATISI minimal terdiri dari 60% dari luar Sumatera Selatan, dan 40% dari Sumatera Selatan. Makalah yang diterbitkan melalui tahap review oleh reviewer yang berpengalaman dan sudah memiliki makalah yang diterbitkan di jurnal internasional yang terindeks SCOPUS.
Arjuna Subject : -
Articles 1,216 Documents
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Design and Development of a Waste Reporting and Monitoring Application to Reduce Open Dumping in Ternate City Muin, Yasir; Lutfi, Salkin
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13716

Abstract

Waste management remains one of the most pressing environmental challenges in many regions, including Ternate City. The persistent practice of open dumping has led to severe environmental and public health impacts. This study aims to design and develop a Waste Reporting and Monitoring Application as a technology-based solution to enhance community participation and improve waste management effectiveness. The system was developed using the Prototype Method, allowing iterative development through continuous user feedback to ensure the final product meets actual needs. The application was built using the Laravel framework and modeled through a Use Case Diagram to represent user interactions within the system. Testing with the Black Box method confirmed that all system functions operated effectively, including report submission, photo uploads, and real-time monitoring. The implementation of this application has increased community engagement in reporting waste accumulation and assisted local authorities in responding more efficiently and systematically. Therefore, the system is expected to serve as a concrete step toward reducing open dumping practices and promoting sustainable environmental management in Ternate City
Komparasi Perhitungan Jarak K-Means Dalam Pengklasteran Tingkat Pengangguran Terbuka di Indonesia Ghoni, Umar; Hidayat, Nur Wahyu; Rakhmawati, Hidayatur; Lestari, Yuniarti
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13750

Abstract

Abstract Unemployment is a short-term economic problem that significantly affects social conditions and public welfare. The Open Unemployment Rate (OUR) serves as an important indicator to measure unemployment levels. In Indonesia, OUR data are periodically published by the Central Bureau of Statistics (Badan Pusat Statistik, BPS) by province and year. Analyzing these data helps identify regions with high or low unemployment rates to support effective labor policy formulation. This study applies the K-Means clustering algorithm to classify Indonesia’s open unemployment rate using four distance metrics: Euclidean, Manhattan, Chebyshev, and Cosine Similarity. The clustering performance was evaluated using the Davies-Bouldin Index (DBI) to determine the best distance metric. The results indicate that Chebyshev Distance produced the best cluster quality with a DBI value of 0.606, while Cosine Similarity yielded the poorest result with a DBI of 1.445. Therefore, Chebyshev Distance is recommended as the most suitable distance metric for clustering Indonesia’s open unemployment rate using the K-Means algorithm. Keywords: open unemployment, K-Means, distance metric, Davies-Bouldin Index.
Perbandingan Algoritma Random Forest dengan K-Nearest Neighbor pada Klasifikasi Tingkat Stress Pelajar Widjaya, Falah Angka; Ngete, Maria Sarina; Tumanggor, Dedi Sapri
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13790

Abstract

One of the most common psychological problems experienced by students is academic stress. This impacts their academic performance, mental health, and their desire to learn. The purpose of this study is to compare the performance of two classification algorithms, namely Random Forest and K-Nearest Neighbor (k-NN), in classifying student stress levels. The data obtained from the Stress Level Dataset on the Kaggle platform consists of 1,100 data points and has 20 attributes covering social, academic, and psychological factors. To ensure stable evaluation results, experiments were conducted using RapidMiner software with a ten-fold cross-validation method. Accuracy, precision, recall, and F1-score were the evaluation parameters used. The results showed that Random Forest achieved 97.55% accuracy with 97.61% precision, 97.53% recall, and 97.56% F1-score. Meanwhile, k-NN only achieved 79.18% accuracy with 83.23% precision, 78.91% recall, and 79.84% F1-score. From the results of this study, it can be concluded that Random Forest is better and more effective in classifying students' stress levels.
Analisis Komparatif Algoritma KNN dan SVM dalam Klasifikasi Tingkat Keparahan COVID-19 Global Syaputra, Rifky Achmad; Dewantara, Moreno; Dwi Putra, Dimas Arya
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13806

Abstract

The severity of COVID-19 varies in each country, requiring an analytical approach that can provide accurate classification as a basis for global health decision-making. Machine learning methods are an effective option for detailing the severity based on data patterns regarding cases, deaths, and other indicators. In this study, the K-Nearest Neighbor (KNN) algorithm was compared with Support Vector Machine (SVM) using a global dataset on COVID-19 taken from Kaggle. The analysis process included data pre-processing, data exploration, model building, and evaluation using accuracy, precision, recall, and F1-score metrics. The results of the evaluation showed that SVM performed better with an accuracy of 87%, while KNN only reached 78%. In addition, SVM also produced a lower and more consistent classification error rate in each severity category. Based on these findings, SVM is considered more efficient in classifying the severity of COVID-19 in globally distributed data that is unevenly distributed.
Pengembangan Sistem Informasi Data Pendeta Gereja Toraja Berbasis Web Menggunakan Framework Laravel Allo Linggi, Genota Pali'
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13825

Abstract

Gereja Toraja is one of the Protestant church denominations in Indonesia, organized under a presbyterial-synodal structure, and spread across 17 provinces with thousands of congregants and hundreds of active pastors. The complexity of its administration and pastor data management requires an efficient, integrated, and accurate system. This study aims to develop a web-based pastor data management information system for the Gereja Toraja using the Laravel framework. The system is designed to facilitate real-time recording, updating, and reporting of data, replacing manual methods that are prone to duplication and data loss. Development follows the Waterfall model through requirements analysis, literature review, system design using UML, implementation, and testing with User Acceptance Test (UAT) techniques. The results indicate that the system improves administrative efficiency, data accuracy, and accessibility for both administrators and pastors. This system provides an effective solution for real-time and structured pastor data management in the Gereja Toraja.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN SALES TERBAIK DI PT MGM MENGGUNAKAN METODE WASPAS Jaya, Jessica Thalia; Kesuma, Dorie P.
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13826

Abstract

PT MGM, an automotive company, currently conducts periodic assessments to determine its best salespersons. This process, however, is performed manually using Microsoft Excel. This process is inefficient, time-consuming, subjective, and lacks transparency for the sales staff being assessed. This research aims to design a decision support system (DSS) to overcome this problem. This system was developed using the Waterfall method, utilizing the PHP programming language and MySQL database for its application development. The Weighted Aggregated Sum Product Assessment (WASPAS) method is used to calculate and rank the best salespersons based on established criteria, such as sales volume, attendance, and performance evaluation. The result of this research is a web-based DSS application capable of managing sales assessment data. Based on user satisfaction testing via questionnaires administered to HRD, ADH, Directors, and Sales staff, the system received a positive response as it functions properly and simplifies the assessment and performance monitoring process. The implementation of this system significantly improves the objectivity, effectiveness, and speed of the assessment process, while also reducing the risk of input errors. This system also provides transparency as sales staff can view their own performance results.
Penerapan Algoritma Random Forest Prediksi Penyakit Paru-Paru Nur Ikhda, Sofi Nissa; Ramdhan, Nur Ariesanto; Premana, Agyztia
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13854

Abstract

Lung disease is a serious respiratory disorder that requires early detection for proper treatment. The advancement of data mining technology enables the diagnostic process by analyzing patient behavioral data to predict the likelihood of developing lung disease. This study applies the Random Forest algorithm using data obtained from Kaggle, containing information such as age, gender, smoking habits, activities, and pre-existing conditions. The dataset was processed using the RapidMiner application through a data preprocessing phase, separating labeled and unlabeled data. A total of 998 records were used for model training and 29,002 for prediction. The model evaluation employed the Performance operator to measure prediction accuracy. The results show that the Random Forest algorithm achieved 94.33% accuracy with high precision and recall values, proving its effectiveness in handling large datasets and supporting early detection of lung disease.
Klasifikasi Stunting Beserta Penerapan Food Combining pada Balita di Desa Sendang Dajah Sayadi, Ahmad; Budhi, Robby Kurniawan; Widianto, Yonatan
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.14082

Abstract

Stunting is a condition characterized by impaired growth in toddlers caused by prolonged nutritional deficiency, which negatively affects physical development, cognitive capacity, and long-term productivity. Early identification of nutritional status using a data-driven classification approach serves as an essential strategy to prevent stunting, particularly in rural areas such as Sendang Dajah Village where access to health services is limited. This study aims to develop a nutritional status classification model for toddlers and generate food combining recommendations tailored to each child’s nutritional needs. The research variables include age, height, weight, and gender. The classification model was constructed using the Support Vector Machine (SVM) algorithm with an 80% training and 20% testing data split. The dataset consists of 300 toddler samples collected through Posyandu and field surveys. The experimental results indicate that SVM successfully classified nutritional status into four categories Severely Stunted, Stunted, Normal, and Tall with an accuracy of 95%, macro average precision of 0.95, recall of 0.96, and f1-score of 0.95, and weighted average precision of 0.96, recall of 0.95, and f1-score of 0.95. These findings demonstrate that SVM is an effective predictive method for early stunting detection and can serve as a decision-support tool for healthcare workers and parents in planning appropriate nutritional interventions.
Implementasi Sistem Pengukuran Kepuasan Masyarakat Berbasis Kuesioner Pada Pelayanan Publik Di Desa Mano'an. Fatli, Fatli; Budhi, Robby Kurniawan; Widianto, Yonatan
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.14160

Abstract

Community contentment serves as a significant measure for evaluating the standard of public services within villages. However, in Mano’an Village, the satisfaction measurement process is still carried out in an unstructured manner, such as through verbal complaints, which does not produce valid data for evaluation and decision-making. This study develops a questionnaire-based community satisfaction measurement system to provide a more objective, directed, and well-documented assessment mechanism. The system is designed to systematically collect data from various types of village services, including administrative, health, education, infrastructure, land affairs, security, and social services. The introduction of an online system allows the village administration to handle information, conduct assessments, and generate clear and straightforward reports. The findings indicate that the system enhances the efficiency of evaluating public services, help the village government understand community needs, and assist in determining service improvement priorities. In addition, the availability of structured data supports transparency, accountability, and increased community participation in village development. Overall, this system is expected to serve as a strategic tool for improving the quality of public services and village governance
Transformasi Pembelajaran Melalui Digital Twin: Systematic Literature Review Lintas Jenjang Pendidikan Thoniyah, Diana Qisthin; Widodo, Suprih; Elviani, Ulva
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.14246

Abstract

Digital Twin (DT) is an innovative technology that creates virtual replicas of physical entities to simulate real-world behavior through real-time data. Although it has been successfully implemented in manufacturing, healthcare, and agriculture sectors, the application of DT in education remains fragmented and focused on specific domains. This study aims to conduct a systematic literature review to identify DT applications in education across various levels, map implementation methodologies, analyze impacts on learning outcomes, and identify challenges and best practices. The method used is a systematic literature review with the PRISMA framework. The search was conducted on the Scopus database for the period 2020-2025 using keywords related to Digital Twin, assessment, and education, yielding 773 articles which were then filtered to 32 final articles. After undergoing quality assessment, 17 articles were selected for further analysis. The research findings indicate that DT enhances students' conceptual understanding and practical skills, supports simulation-based learning, personalized learning, and remote collaboration. These findings provide comprehensive insights into DT utilization that can serve as a reference for researchers, educational practitioners, and policymakers in optimizing the implementation of this technology to improve educational quality.

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