JURNAL JUMATIKO

IMPLEMENTASI PERANCANGAN SISTEM PEMINJAMAN BUKU PERPUSTAKAAN BERBASIS WEB DI SMA NEGERI 8 PADANGSIDIMPUAN MENGGUNAKAN METODE LAST IN FIRST OUT (LIFO)

Mariani Sembiring — 2026-01-07

In today’s modern era, technological advancements have made it easier to access electronic books from anywhere and at any time. However, physical books still play an important role, as not everyone is comfortable reading for long periods on digital screens. Previous studies have shown that only a small portion of users can sustain prolonged reading on digital devices. Therefore, libraries remain a primary source of physical books and cannot be overlooked. One of the main challenges is that many libraries continue to depend on manual systems for recording book borrowings, which are prone to damage, loss, and make data retrieval difficult especially when the number of borrowings is high. To address this issue, the author has designed a web-based book lending system to assist both library staff and users. Referring to previous research, digital libraries have proven to offer many benefits. Building on this, the author developed a web-based lending system that incorporates the LIFO (Last In First Out) algorithm, where the most recently returned book is lent out first. Based on observations at SMA Negeri 8 Padangsidimpuan, the system provides several advantages: for staff, it simplifies stock checking, borrower tracking, and administration; for book quality, it helps preserve better-condition books by not lending all copies; and for readers, it speeds up the borrowing process in the library.

APLIKASI FUZZY LOGIC MENGGUNAKAN METODE MAMDANI UNTUK PENENTUAN PENERIMA BEASISWA DI SDN 100111 HUTATUNGGAL

Desi Fitrah Nainggolan — 2026-01-22

Objective and accurate scholarship recipient selection is essential in the education sector, particularly at the elementary school level such as SDN 100111 Hutatunggal. This research develops a decision support system using the Mamdani fuzzy logic method to assist the school in evaluating students’ eligibility for scholarships based on three main criteria: parental income, number of dependents, and academic grades. Each criterion is modeled using triangular membership functions and processed through 27 fuzzy rules to generate a final eligibility score. The system is implemented using MATLAB R2025a, complete with a user-friendly graphical user interface (GUI) for ease of operation. Testing results demonstrate that the method provides consistent, fair, and accountable decisions. Moreover, the system enhances time efficiency in the selection process and reduces subjectivity in evaluation. Therefore, the application of Mamdani fuzzy logic proves to be an effective tool for decision-making in determining scholarship recipients in primary school settings, particularly at SDN 100111 Hutatunggal.

SISTEM INFORMASI MANAJEMEN ANTRIAN PASIEN BERBASIS WEB DI BIDAN FATIMAH SIREGAR, AMD.KEB, PARGARUTAN SOSOPAN, KECAMATAN ANGKOLA TIMUR, KABUPATEN TAPANULI SELATAN MENGGUNAKAN METODE FIRST IN FIRST OUT (FIFO)

Dini Mardiah Pohan — 2026-01-22

According from BPS data (2025), the majority of people in North Sumatra prefer medical treatment from doctors or midwives, with 45.41% of males and 44.93% of females choosing this option. This indicates a high level of public trust and satisfaction. However, challenges such as long queues, concerns about losing one’s turn, and uncertain clinic operating hours can reduce patient comfort. To address these issues, the author developed an online queue management system that allows patients to take queue numbers and check clinic operating status in real time. Previous studies have shown that web-based queue applications improve efficiency and patient convenience, as well as reduce waiting time and increase user satisfaction. Based on these findings, this study combines a clinical information system with the FIFO (First In First Out) method, which ensures fairness by serving patients in the order they register. The implementation at Bidan Fatimah Clinic has proven to enhance service convenience and patient satisfaction, as patients previously experienced disappointment when arriving to find the clinic closed due to the midwife’s absence. With the web-based queue management system, patients can now check clinic availability and decide whether to visit or postpone treatment based on queue conditions.

IMPLEMENTASI K-MEANS UNTUK KLASTERISASI TINGKAT KEPARAHAN PENYAKIT PERIODONTAL BERDASARKAN DATA KLINIS

Lamria Pasaribu — 2026-01-23

Technological developments have had a significant impact in various fields, including health. In the field of dental health, one of the most common diseases is periodontal disease. Periodontal disease is a multifactorial disease that occurs in the supporting tissues of the teeth. The assessment of the severity of periodontal disease is often done subjectively. Therefore, a method is needed to help classify patient data based on the severity of periodontal disease in order to plan appropriate treatment based on the severity of the disease. This study uses the K-Means clustering algorithm. Clustering is performed based on gender, age, mean probing depth, mean clinical attachment loss, plaque index, and bleeding on probing. This study used the RapidMiner application and resulted in three levels of disease severity: mild, moderate, and severe. The evaluation results using the Davies-Bouldin Index showed a value of 0.688, indicating that the clusters formed had a good level of separation.

EKSPLORASI ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK VALIDASI INFORMASI DAN DETEKSI RUMOR PADA ARTIKEL BERITA

Leni Mardiah Lubis — 2026-01-23

The spread of hoaxes and rumors in digital media has become a serious issue that requires technological solutions for automatic detection. This study aims to explore a text-based news classification system using a Convolutional Neural Network (CNN) architecture to detect information validity and rumors in news articles. The research methodology includes data collection from various online sources, text preprocessing, tokenization, CNN model training, and system testing. The dataset used consists of 1,000 news articles, divided into 500 valid news articles and 500 invalid (hoax) articles. Data were collected from press council–verified news outlets for the valid category and from anonymous sources for the hoax category. Preprocessing steps include text cleaning, tokenization, padding, and label encoding. The CNN model was designed with embedding layers, 1D convolution, global max pooling, dropout, and dense layers for binary classification. The results show that the CNN model achieved an accuracy of 85–90% in classifying valid and invalid news. The model demonstrated good performance in recognizing factual news from credible sources but faced challenges with hoax articles written in formal language styles. The system is capable of making predictions in real time as well as in batch processing through Excel files, making it practical for large-scale implementation.