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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
 113   114   115   116   117 
Studi Komparatif Teknik Cropping Urat Daun Jeruk dengan Metode Artificial Neural Network irfani, muhammad haviz
JATISI Vol 12 No 2 (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.v12i2.11429

Abstract

Citrus as a major agricultural commodity in Indonesia, plays a crucial role in the industry and farmers' income. Identification of citrus seedling types is a major challenge, due to the lack of knowledge and experience of farmers, causing potential financial and time losses. This study compares the Artificial Neural Network Backpropagation (JST-PB) method and Gray Level Co-occurrence Matrix (GLCM) features in orange seedling type identification through leaf vein images. Data was collected using a macro camera with Samsung ISOCELL GM2 sensor, with various cropping sizes on a total dataset of 1250 training images and 625 test images. The JST-BP method and GLCM features provided an accuracy rate of 91.2% at a cropping size of 200x200 piksels, 87.2% at a cropping size of 250x250 piksels, 90.4% at a cropping size of 300x300 piksels, 95.2% at a cropping size of 350x350 piksels, and the highest accuracy rate at a cropping size of 400x400 piksels, reaching 98.4%. The results of this study make an important contribution to the understanding of the identification of citrus seedling types through leaf vein images, highlighting the comparison between the JST-PB method and GLCM features at various image cropping sizes.
Desain Mini Soil Moisture Sensor Untuk Deteksi Kekeringan Tanaman Pot Sari, Herva Emilda; Priyono, Hary; Tumanggor, Benelekser
JATISI Vol 12 No 2 (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.v12i2.11481

Abstract

Technological advances in today's digital era are the latest breakthroughs that are a way out of many existing problems. The community is a part that needs technological updates, which can facilitate large and small daily tasks. Like gardening tools in ancient times required a complicated process in caring for plants, but now caring for plants can be easy because of the existence of technological tools that can help care for the plants. Like the simplest thing is watering plants, if you forget it can make the plants wilt and look stale or even die. The Mini soil moisture sensor is designed to provide a warning of soil dryness in potted plants indoors. This tool provides a warning to the owner of the ornamental plant pot, that the soil in their favorite plant pot is dry. The design of the humidity detection sensor tool is used to detect a lack of water in the soil and send voltage to the LED. So that if no water is detected in the soil that is embedded in this tool, the LED will light up. The warning light from the LED on this tool is part of the Mini soil moisture sensor design to be able to provide a warning to the owner of ornamental plants about their plants. So that plants that are cared for indoors can safely get enough water from the Mini soil moisture sensor warning design.
PENERAPAN K-MEANS CLUSTERING MENENTUKAN LOKASI PROMOSI PENERIMAAN MAHASISWA BARU UNIVERSITAS MUHAMMADIYAH BANJARMASIN Hasanah, Nor Laila; Ningrum, Ayu Ahadi; Kamarudin, Kamarudin
JATISI Vol 12 No 2 (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.v12i2.11488

Abstract

Strategi promosi yang efektif menjadi kebutuhan penting bagi perguruan tinggi dalam menghadapi persaingan penerimaan mahasiswa baru. Penelitian ini bertujuan untuk mengelompokkan asal sekolah calon mahasiswa baru Universitas Muhammadiyah Banjarmasin (UMB) menggunakan algoritma K-Means Clustering berbasis data pendaftaran tahun akademik 2019 hingga 2025. Proses penelitian mengadopsi pendekatan Knowledge Discovery in Database (KDD) yang mencakup tahap seleksi, pra-pemrosesan, transformasi, data mining, dan evaluasi. Penentuan jumlah klaster optimal dilakukan dengan empat metode evaluasi internal, yaitu Elbow Method, Silhouette Score, Davies-Bouldin Index, dan Calinski-Harabasz Index, dengan hasil terbaik pada K=4. Hasil pengelompokan menghasilkan empat kategori potensi sekolah, yaitu Sangat Berpotensi, Berpotensi, Sedang, dan Rendah. Sekolah dalam kategori Sangat Berpotensi terkonsentrasi di wilayah Kalimantan Tengah, sedangkan kategori lainnya tersebar lebih merata di Kalimantan Selatan dan daerah lain. Temuan ini menunjukkan bahwa pemetaan berbasis data dapat menjadi acuan strategis dalam menyusun promosi wilayah yang lebih efektif dan terarah.
Optimalisasi Pengelompokan Gangguan Kecemasan dalam Mendukung Tujuan Pembangunan Berkelanjutan Menggunakan Algoritma K-Means dan K-Medoids Aulia, Rahma; Julianti, Nadea; Putri, Siti Faradila; Efrizoni, Lusiana; Deni, Rahmad
JATISI Vol 12 No 2 (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.v12i2.11495

Abstract

Abstract Data clustering is a data mining technique that aims to find hidden patterns in a dataset. The dataset used in this study was taken from the Kaggle public dataset on anxiety attacks. Anxiety disorder is a mental condition characterized by excessive and prolonged feelings of anxiety. Clustering anxiety disorders facilitates finding the cause, effect, and better treatment. Therefore, this study aims to group anxiety disorders using the K-Means and K-Medoids algorithms by considering attributes such as stress level, sleep patterns, and physical activity. The performance of the model is evaluated using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm produced the lowest DBI value in cluster ten with an accuracy value of 2.331. This shows that the K-Means algorithm is able to identify significant patterns in anxiety disorder data. This study can be a recommendation for health professionals in making more precise diagnoses, understanding the characteristics of the causes of anxiety disorders. In addition, this study also supports the achievement of the Sustainable Development Goals in an effort to improve the overall health and welfare of the community. Keywords— K-Means, K-Medoids, Anxiety Disorders, Sustainable Development Go als
Sistem Informasi Posyandu ABC Desa Cit Terhadap Peningkatan Pelayanan Kesehatan Ibu dan Anak Probonegoro, Wishnu Aribowo; Sari, Lili Indah
JATISI Vol 12 No 2 (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.v12i2.11591

Abstract

Posyandu is a basic health service for people in villages in Indonesia, but posyandu focuses more on babies, toddlers, mothers, especially pregnant and breastfeeding mothers, where services are usually carried out every month. Posyandu abc services in Cit village are still very simple and still manual, starting from registration, data recording, examination, immunization and reports made, all of which are still done manually, so that the services provided are slow and less than optimal, long queues and limited officers at the posyandu. Errors in the data recording process also often occur. The problems that occurred at the posyandu abc in Cit village attracted the author to conduct research and help provide solutions by creating a posyandu information system that can be used, in order to facilitate and smooth services for mothers and children. In this study, the author used a qualitative descriptive method and the Waterfall method as a method for developing the system in order to support this research. It is hoped that with the existence of the posyandu abc information system in Cit village, data processing, the registration process for maternal and child health services can be faster, measurable, effective and efficient. Facilitates data storage and access
Prediksi Keberhasilan Adopsi Rapor Digital Madrasah Pendekatan Terpisah menggunakan Algoritma Naive Bayes dan Decision Tree dengan Pruning Jamaluddin, Jamaluddin; Yonhendri, Yonhendri
JATISI Vol 12 No 2 (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.v12i2.11635

Abstract

MTs Negeri 1 Batam as one of the Islamic educational institutions in Indonesia is a part that has adopted digitalization. Rapor Digital Madrasah is one form of digitalization launched by the Ministry of Religious Affairs as an effort to modernize the academic reporting system. This study aims to predict the success rate of Madrasah Digital Report Card adoption using different machine learning approaches, namely Naive Bayes algorithm and Decision Tree with pruning. The approaches were separated through each algorithm's specific preprocessing pipeline, with Naive Bayes using numerical feature standardization and Decision Tree without standardization. Results showed Decision Tree performed higher in each evaluation metric, with 98% vs. 92% accuracy and 100% vs. 83% minority class recall. Feature importance analysis identified three main factors influencing successful adoption, namely Technical Constraints (38.4%), Frequency of Use (24.7%), and Digital Experience (18.2%). Overall, the adoption of digital report cards was successful with a success rate of 86.3%. The results of this study can be implemented in the development of more effective and targeted intervention strategies in the future in increasing the success of Madrasah Digital Report Card adoption throughout Indonesia.
Implementasi Preorder Traversal Dalam Rekomendasi Produk Toko Bangunan Dunia Baru Elizabeth, Triana; Tinaliah, Tinaliah
JATISI Vol 12 No 2 (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.v12i2.11711

Abstract

Toko Bangunan Dunia Baru Palembang has implemented a sales application system since 2018 to improve operational efficiency. Although a discount feature has already been included, the application still lacks an automatic product recommendation feature. In fact, such a feature is essential for boosting sales by suggesting related products that are often purchased together. This research aims to design and develop a product recommendation application using a binary tree data structure with the preorder traversal algorithm. This method enables the system to provide product suggestions based on the hierarchical relationship between main products and supporting products. The research was conducted through literature review, direct observation, collecting sales data, grouping data based on product categories and co-purchase frequency, designing the tree data structure, implementing the algorithm, and testing the application. The test results show that the feature is easy to use for employees and can assist them in providing quick and logical product recommendations. However, some issues were identified, such as the font size being difficult to read and the lack of product details—such as brand and stock availability—on the same display.
Pengaruh Moderasi Kesiapan Teknologi Pada Penggunaan E-Learning di Kalangan Pelajar Studi Kasus : SMP Sutomo 1 Medan Budi, Setia; Panjaitan, Erwin Setiawan; Nurjanah, Sofiana
JATISI Vol 12 No 3 (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.v12i3.11536

Abstract

This research aims to investigate the moderating effect of technology readiness on the use of E-learning among students, with a focus on a case study at SMP Sutomo 1 Medan. E-learning has become an increasingly popular method of learning, especially in the context of distance education. The Delone and McLean theory is used as the theoretical framework to examine the use of E-Learning and technology readiness, consisting of variables such as optimism, innovativeness, discomfort, and insecurity as moderation variables. The research method is quantitative, and the data source is obtained through a questionnaire from 332 students at SMP Sutomo 1 Medan. Data analysis is conducted using SmartPLS 3 and hypothesis testing. The research results indicate accepted hypotheses in the relationships between Information Quality and Intention to use, Service Quality and Intention to use, Information Quality and User Satisfaction, Service Quality and User Satisfaction, User Satisfaction and Net Benefit, and the optimism variable moderating System Quality on Intention to use. Rejected hypotheses include System Quality with Intention to use, and the moderation variables of innovativeness, discomfort, and insecurity that do not influence the relationship between System Quality and User Satisfaction
Penerapan Algoritma K-Medoids dalam Menganalisis Pola Pelanggan untuk Strategi Pemasaran De Pani, Raihan; Putri, Daffina Zahro; Ginting, Steven; Ginting, Lusiana; Rahmaddeni, Rahmaddeni
JATISI Vol 12 No 3 (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.v12i3.11542

Abstract

Customer segmentation is a strategic approach in data-driven marketing, allowing businesses to identify purchasing patterns to enhance the effectiveness of marketing campaigns. This study implements the K-Medoids algorithm to analyze customer behavior based on transaction data to form more accurate customer clusters. The data used was obtained from transaction history and underwent preprocessing steps such as cleaning and normalization. The clustering process was conducted by determining the optimal number of clusters using the Elbow Method and evaluated with the Silhouette Score. The results indicate that the optimal number of clusters is two, with a Silhouette Score of 0.5602, demonstrating well-separated clusters. Based on the clustering results, marketing strategies can be optimized by adjusting loyalty programs and providing personalized product recommendations to enhance customer engagement. With this approach, businesses can improve customer satisfaction and the efficiency of data-driven marketing. Keywords: K-Medoids, Clustering, Customer Segmentation, Marketing Strategy
ANALISIS PERAMALAN PENJUALAN AIR MINUM DALAM KEMASAN DISTRIBUTOR AIR AMANAH DENGAN METODE NAÏVE BAYES naufal, muhammad muhammad; Kamarudin, Kamarudin; Windarsyah, Windarsyah
JATISI Vol 12 No 3 (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.v12i3.11545

Abstract

This study aims to assist the Amanah Water Distributor in efficiently managing its stock of bottled drinking water by applying the Naïve Bayes method. This method analyzes historical sales data from 2023 to 2024 to accurately forecast monthly stock requirements. The developed forecasting system provides restocking recommendations to prevent overstocking—which can increase storage costs—and understocking—which may lead to lost sales opportunities. With more accurate forecasting, the system is expected to improve operational efficiency, minimize the risk of loss, and support more effective decision-making to ensure the sustainability of the Amanah Water Distributor’s business operations.

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