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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 1,236 Documents
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Klasifikasi jenis topeng pajegan berdasarkan pola ukiran menggunakan Convutional Neural Network ida bagus surya wangsa
JATISI Vol 13 No 1 (2026): 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.v13i1.15184

Abstract

Topeng Pajegan is a Balinese cultural heritage distinguished by specific visual carving patterns and holds significant aesthetic, philosophical, and religious value. However, its classification is still conducted manually and depends on individual expertise, making it prone to subjectivity and limited scalability, while digital documentation remains limited. This study proposes a classification system for Balinese Topeng Pajegan using a Convolutional Neural Network (CNN) to support cultural digitalization and preservation. The system was developed following the CRISP-DM methodology, utilizing a dataset from Kaggle complemented by authentic data collected from Balinese mask artisans, dancers, and collectors. Model optimization was performed through data augmentation and RandomSearch-based hyperparameter tuning. Experimental results demonstrate that the optimized CNN model successfully classified six types of Topeng Pajegan with an accuracy of 90.12%, supported by F1-score and confusion matrix evaluations. The model was implemented in a web-based application, which functions as both an automated classification tool and a digital educational platform to promote the sustainable preservation of Balinese cultural heritage. Keywords: Topeng pajegan, Convolutional Neural Network, Computer Vision
Rancangan Bangun Sistem Informasi Pembuatan Akta PPAT dan Notaris Berbasis Web Pada Kantor Notaris Batara Wardana Yuswar
JATISI Vol 13 No 1 (2026): 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.v13i1.15530

Abstract

Teknologi informasi memberikan kemudahan dalam pelaksanaan tugas dan membantu kita menghasilkan data yang akurat dan efektif. Salah satu aplikasi dalam bidang ini adalah sistem pembuatan akta di Notaris/PPAT. Namun, saat menggunakan sistem pembuatan akta, terdapat tantangan yang mengakibatkan keterlambatan pekerjaan, kesalahpahaman, dan kesulitan dalam memperbaiki data yang salah, disebabkan oleh sistem pembuatan akta yang belum diperbarui dengan teknologi informasi terkini. Untuk menyelesaikan permasalahan ini, peneliti mengembangkan dan menciptakan sistem informasi pembuatan akta untuk PPAT dan Notaris di kantor Notaris/PPAT Marlina, SH yang berbasis web dengan memanfaatkan Laragon, MySQL, HTML, Bootstrap, PHP, Framework Laravel, dan Livewire. Penelitian ini mengimplementasikan pendekatan Penelitian dan Pengembangan menggunakan model pengembangan sistem Waterfall. Selain itu, penelitian ini juga menerapkan metode selection sort untuk mengurutkan proses akta berdasarkan waktu yang mendekati atau melebihi batas yang telah ditentukan. Hasil dari penerapan ini adalah data proses akta yang terurut sesuai dengan batas waktu yang mendekati atau melampaui waktu yang telah ditetapkan, serta laporan akta bulanan dan laporan akta tahunan. Sistem ini diharapkan bisa membantu dalam mengurangi potensi terjadinya kekeliruan, mempermudah tugas, serta meningkatkan efisiensi dalam pembuatan akta PPAT dan Notaris.
Rancang Bangun Aplikasi Berbasis Android Klasifikasi Penyu dengan Menggunakan Metode Extreme Programming I Made Agus Priatna Putra Arnata; I Gede Juliana Eka Putra; I Nyoman Yudi Anggara Wijaya
JATISI Vol 13 No 1 (2026): 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.v13i1.15539

Abstract

The turtle population in Indonesia continues to decline every year and is categorized as an endangered species. Lack of public knowledge about protected turtle species is one of the factors hampering conservation efforts. This study aims to design and build an Android-based application that can classify three types of turtles: the Green Turtle (Chelonia mydas), the Hawksbill Turtle (Eretmochelys imbricata), and the Olive Ridley Turtle (Lepidochelys olivacea). Software development uses the Extreme Programming (XP) method, consisting of planning, design, coding, and testing stages. The application is built with the Flutter framework and integrates a Convolutional Neural Network (CNN)-based machine learning model to classify images. Users can input images through the camera or device gallery. Blackbox testing results show that all application functionality, including navigation, image capture, and the classification process, runs as expected. This application is expected to be an educational medium to increase public awareness in turtle conservation efforts. Keywords— Classification, Turtle, Flutter, Android, Extreme Programming
Penerapan Algoritma Convolutional Neural Network untuk Estimasi Volume Sampah Berbasis Analisis Citra Digital di Kota Tasikmalaya Agus Supriatman
JATISI Vol 13 No 1 (2026): 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.v13i1.15640

Abstract

This research addresses the escalating global issue of waste management, particularly in developing cities like Tasikmalaya, Indonesia. Despite a daily waste production reaching 320 tons, the current management system remains manual and reactive. This study aims to transform this system into a proactive model by implementing Digital Image Analysis and a Convolutional Neural Network (CNN) algorithm to predict waste volume from images. The methodology involved the collection and augmentation of a waste image dataset, yielding a total of 3,168 images. Furthermore, a sequential CNN architecture was designed and trained over 50 epochs. The primary novelty and finding of this research lie in the developed CNN model, which achieved a high overall accuracy of 90% in volume classification. This performance demonstrates that computer vision provides an effective solution, significantly outperforming the 77.6% accuracy reported in a previous related study. Ultimately, this achievement marks a crucial step toward realizing a Smart Waste Management System. It establishes a data-driven foundation for optimizing collection schedules and resource allocation, despite minor challenges in distinguishing between highly similar volume classes (e.g., 90% and 100%).
Desain Desain Framework Gamifikasi Untuk Pengobatan Tuberkulosis Paru Pada Pasien Anak Ivan Rachmawan
JATISI Vol 13 No 2 (2026): 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.v13i2.475

Abstract

The risk of failure of anti-tuberculosis drug therapy (OAT) especially for children with TB is very high. This is because pediatric TB patients experience boredom with the obligation to take OAT every day without interruption in the long term (6-8 months). Factors inability of children to express symptoms of the disease they are suffering and side effects of OAT also cause pediatric TB patients to be disobedient and refuse to undergo OAT therapy. Integration of game elements into the therapeutic process is a solution proposed in this study or often called gamification. Gamification is the use of game elements in non-game contexts that are expected to increase motivation, user involvement and change behavior. This study aims to create a gamification design to improve treatment compliance in children with pulmonary TB.
Protection of Personal Data in Mobile Health: a mini Review Nurbaiti Nurbaiti
JATISI Vol 13 No 2 (2026): 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.v13i2.566

Abstract

Mobile Health (mHealth) is recognized as an innovative approach to providing accessible, portable and cost-effective healthcare. Despite the many benefits associated with using a mobile device, there are major concerns about mhealth in the area of privacy and security. The purpose of this article is to find out how to protect personal data in the form of data security and privacy in personal health devices. The method used is a simple article review or mini literature review to find answers to scientific questions that match the purpose of this article using a database (PubMed, Researchgates and Google Scholar) with the literature of the last 10 years. The results of this study highlight some of the major weaknesses of mHealth. Most of the security systems are still weak, so endanger user privacy and security by violating data protection regulations.
Web Service Untuk Integrasi Sistem Informasi Penelitian UIN Sunan Kalijaga Yogyakarta Daru Prasetyawan
JATISI Vol 13 No 2 (2026): 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.v13i2.3969

Abstract

Data integration is a process to ensure the availability, accuracy, speed and accuracy of data by combining data from several different sources. Data integration is needed to align data, so that every different system will have the same data. Web service is a web application that is used to exchange messages/information via the internet. Interaction between applications or systems using a set of open standard protocols. This research method refers to the software development life cycle (SDLC) for developing web services. The web service was developed using the REST architecture for research data integration using PHP as the programming language and PostgreSQL as the DBMS. Testing is carried out to ensure that the web service can run as planned. Furthermore, the web service is applied to research information systems. The web service is used to facilitate communication between research information systems and other applications.
the Forensic Test Information System Firearms Ammunition Protective Clustering Process Using Gray Level Co-occurance Matrix and K-Mean Clustering Algorithms didik supriyadi
JATISI Vol 13 No 2 (2026): 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.v13i2.8282

Abstract

The use of technology is a solution when developments continue to increase and develop. The connection between technology and the field of state security is no exception. The method that supports clusterization is feature extraction using the Gray Level Co-occurrence Matrix (GLCM), carried out before the clusterization process. GLCM is suitable for extracting features or characteristics in images with unique patterns, such as puppet pattern recognition research. This research procedure is a flow chart to build an information system for forensic testing of the clustering process for firearm ammunition projectiles using the Gray Level Co algorithm. -occurrence Matrices (GLCM) and K-means clustering. Figure 3.1 below illustrates the information system framework as an explanation of each input flow, process, and output. The research results show that the GLCM method for feature extraction from grayscale images and the K-Means method for clustering provide good results and accuracy. The model performance reached 71.14% even with limited data. This model can be used in console applications such as Google Collabs and with a GUI with relatively stable application performance.
Deteksi Objek Deteksi Telur Ayam Berdasarkan Citra Cangkang Menggunakan You Only Look Once (YOLOv5) anthony alexander roses
JATISI Vol 13 No 2 (2026): 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.v13i2.8372

Abstract

Pengidentifikasian dan pengenalan objek dalam bidang computer vision sedang mengalami perkembangan pesat dan diaplikasikan dalam berbagai bidang, mulai dari industri hingga sektor kesehatan. Dalam industri peternakan khususnya peternak telur ayam ras, seringkali terdapat perbedaaan warna pada cangkang telur. secara tradisional, warna pada cangkang telur ditentukan secara visual oleh peternak berpengalaman berdasarkan warna pada cangkang telur itu sendiri. Namun, teknik ini seringkali masi terdapat kelemahan karna banyaknya cangkang telur yang warnanya hampir sama yang bersifat subjektif. Berdasarkan hal ini maka dibuatlah sistem yang dapat membantu untuk mendeteksi telur ayam berdasarkan citra cangkang menggunakan Yolo v5. Tujuan penelitian ini adalah untuk membantu mengidentifikasi telur ayam dengan Algoritma Yolo v5. Dataset yang digunakan terdiri dari 608 citra dan data anotasi dibuat dengan roboflow. Hasil akhir dari dari pengujian dalam penelitian ini di bagi menjadi dua bagian yakni pengujian dengan learning rate 0.1, dan pengujian menggunakan learning rate 0.01. Berdasarkan hasil evaluasi, untuk pengujian menggunakan learning rate 0.1, rata-rata nilai precision mencapai 98,6%, recall mencapai 98,5%, dan mAP mencapai 99,3%. Sedangkan untuk pengujian menggunakan learning rate 0.01, rata-rata nilai precision mencapai 98,9%, recall mencapai 97,5%, dan mAP mencapai 99%.
Penerapan Algoritma GLCM dan KNN Pada Pengenalan Olahan Daging Ikan Dalam Pembuatan Pempek Palembang jamiul huda
JATISI Vol 13 No 2 (2026): 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.v13i2.8846

Abstract

Indonesia is a nation rich in a variety of cultures ranging from customs, culture, livelihoods, social, even to culinary has become a specialty of this nation. One of them is pempek or commonly called empek-empek. A snack originating from the Palembang area, South Sumatra is very popular among Indonesians from the lower class, middle class, to the elite who are usually used as a side dish. Pempek is made from softly ground fish meat mixed with starch or sago flour, and with the addition of other ingredients including eggs, garlic, flavoring and salt. Currently, many household industries (IRT) in processing fish meat are still not in accordance with the authenticity of Palembang so that the creation and taste of pempek from one industry to another will be different. In this research, KNN (K-Nearest Neighbors) algorithm is juxtaposed with GLCM (Gray Level Co-accurence Matrix) algorithm in processing the image of pempeki dough. By using GLCM and KNN algorithms, this research aims to identify the processed dough that matches the original Palembang pempek snacks where the taste and texture are like the original. The GLCM algorithm is used to extract patterns from the image of pempek dough. In this study, the accuracy obtained from the KNN algorithm is 83% by using the K value of K = 3, K = 5, K = 9, K = 11.

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