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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Teknika Jurnal IPTEK Jurnal KARISMATIKA Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Kridatama Sains dan Teknologi bit-Tech Jurnal Pendidikan Teknologi Informasi dan Vokasional JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Informasi dan Sains Teknologi SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Soft Computing Exploration Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Teknik: Jurnal Ilmu Teknik dan Informatika INCODING: Journal of Informatics and Computer Science Engineering Instal : Jurnal Komputer Formosa Journal of Science and Technology (FJST) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Matematika, Komputasi dan Statistika Blend Sains Jurnal Teknik QISTINA: Jurnal Multidisiplin Indonesia Journal of Education Technology Information Social Sciences and Health Jurnal Indonesia Sosial Teknologi Innovative: Journal Of Social Science Research Jurnal Multidisiplin Teknologi dan Arsitektur Journal of Informatics and Data Science (J-IDS) Jurnal Komputer dan Teknologi (JUKOMTEK) Jurnal Ilmiah Sistem Informasi Journal of Accounting Law Communication and Technology Journal of Education Transportation and Business Journal of Computer Science Advancements
Zulfahmi Indra, Zulfahmi
Uniersitas Negeri Medan
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Handwritten Batak Toba Script Recognition Based on Deep Learning Using the Convolutional Neural Network (CNN) Algorithm Samosir, Wahyu Ardiantito; Zulfahmi Indra; Insan Taufik; Susiana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 1 (2025): October 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i1.1795

Abstract

The Batak Toba script is one of Indonesia’s cultural heritages that has become increasingly rare and less recognized among younger generations. This research aims to develop a handwriting recognition system for Batak Toba characters using the Convolutional Neural Network (CNN) method, capable of accurately recognizing characters, transliterating them into Latin script, and translating them into Indonesian. The dataset was self-generated using the Noto Sans Batak font and character combinations, totaling 113 labels, which were processed into 64×64 grayscale images. The CNN model was designed with several convolutional and pooling layers and compiled using the Adam optimizer and categorical cross-entropy loss function. Training results achieved a validation accuracy of 98.36% and a testing accuracy of 98.12%, with respective loss values of 0.0268 and 0.0295. The system was then integrated into a web-based application built as a Progressive Web App (PWA), supporting both online transliteration and translation features. These results demonstrate that the CNN approach is highly effective in recognizing Batak Toba characters. In the future, the system can be further developed into a full sentence-level OCR, integrated into a native Android application, and expanded with datasets from real handwritten samples.
Implementasi Algoritma K-Nearest Neighbors (KNN) dalam Deteksi Dini Hipertensi berdasarkan Analisis Tekanan Darah Siregar, Ary Prandika; Al Idrus, Said Iskandar; Indra, Zulfahmi; Taufik, Insan
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.1018

Abstract

This study aims to develop a web-based hypertension detection system using the K-Nearest Neighbors (KNN) algorithm and to analyze its accuracy in classifying hypertension status. The dataset was obtained from 447 patient medical records at RSKG Rasyida, consisting of eight variables: gender, age, systolic blood pressure, diastolic blood pressure, height, weight, body mass index (BMI), and hypertension status. The preprocessing stage involved three main steps—feature selection (age, systolic and diastolic blood pressure, BMI), data balancing using undersampling, and data normalization through the Min-Max method—resulting in 425 balanced data samples with five hypertension categories. The web application includes modules for login, dashboard, data input, detection results, and detection history, and has been evaluated using black box testing. The best KNN performance was achieved at k = 13 with 92.94% accuracy, 94% precision, 93% recall, and 93% F1-score. These results indicate that the proposed system can accurately classify hypertension and serve as an effective, data-driven screening tool for healthcare professionals.
Implementasi Algoritma K-Means Clustering Dengan Pendekatan Active Learning Pada Siswa SMA Untuk Menentukan Jurusan Ke Perguruan Tinggi Palevi, Muhammad Rheza; Indra, Zulfahmi
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 23 No 1 (2024): Februari 2024
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v23i1.9553

Abstract

Menentukan jurusan tepat yang akan diambil pada jenjang perguruan tinggi sangatlah penting diperhatikan oleh siswa SMA. Kesalahan dalam memilih jurusan akan menyebabkan siswa tidak maksimal selama berada di perguruan tinggi. Oleh karena itu, penelitian ini dilakukan untuk membantu siswa dalam menentukan jurusan ke perguruan tinggi menggunakan algoritma K-Means Clustering dengan penerapan Active Learning in Machine Learning. K-Means Clustering dengan penerapan Active Learning digunakan untuk menentukan jurusan yang tepat dengan menggunakan data kuesioner dan nilai rapot yang didapat dari pihak sekolah. Pada penelitian ini, didapat hasil bahwa 113 siswa yang menjadi responden terbagi kedalam 12 kategori atau klaster pada bidang ilmu yang berbeda-beda. Secara singkat, penerapan algoritma K-Means Clustering dengan pendekatan Active Learning menghasilkan akurasi 0,059 dan membutuhkan perbaikan terhadap data yang digunakan agar mencapai akurasi yang lebih baik
Application of Graph Coloring on Nurse Work Scheduling at H. Adam Malik Hospital Medan Using the Tabu Search Algorithm Ananda, Rizky; Indra, Zulfahmi; Nasution, Hamidah
ZERO: Jurnal Sains, Matematika dan Terapan Vol 6, No 1 (2022): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v6i1.12451

Abstract

Complex problems that usually occur in every hospital, one of which is scheduling with so many aspects, for example: the number of nurses, the distribution of nurse shifts, time off or leave and others. With the manual method that is still used in compiling the nurse's work schedule, it makes it difficult for an irregular and regular schedule. In solving the scheduling problem, the graph coloring method can be used. This scheduling problem can be solved by graph coloring. One solution to solve the problem of concluding graphs in scheduling is the Tabu Search Algorithm. A method that works as an effective problem solving method in finding the best solution to a problem. A method is used to solve the problem by making a representation in the form of a graph where the nurse is a node and grouping nurses as an edge by implementing the graph coloring into the Tabu Search Algorithm.
Sistem Pakar Diagnosa Penyakit Ginjal Menggunakan Metode Dempster Shafer Di Rsud Pirngadi Medan Parapak, R Putri Angela; Kana Saputra S; Nasution, Hamidah; Indra, Zulfahmi; Taufik, Insan
Innovative: Journal Of Social Science Research Vol. 4 No. 5 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i5.15895

Abstract

Dalam era modern yang dipenuhi dengan kemajuan teknologi komputerisasi, perkembangan teknologi informasi terjadi dengan kecepatan yang luar biasa dan memiliki dampak yang signifikan dalam berbagai aspek kehidupan manusia. Dalam konteks ini, penting untuk diakui bahwa penyakit ginjal merupakan salah satu masalah kesehatan yang perlu mendapatkan perhatian serius dari masyarakat. Sayangnya, penyakit ini seringkali sulit dideteksi secara dini dan dapat mengancam nyawa seseorang jika tidak ditangani dengan tepat. Dalam situasi di mana kesadaran akan kesehatan seringkali rendah, terdapat kebutuhan yang mendesak untuk mengembangkan solusi yang dapat membantu meningkatkan deteksi dini dan diagnosis penyakit ginjal. Salah satu solusi yang inovatif adalah dengan memanfaatkan teknologi informasi dalam bentuk aplikasi sistem pakar. Melalui pendekatan ini, penulis merancang sebuah aplikasi sistem pakar yang bertujuan untuk mendeteksi penyakit ginjal akut dan kronis. Aplikasi ini dibangun dengan menggunakan metode Dempster-Shafer, sebuah teknik yang mampu menggabungkan data dari berbagai sumber untuk menghasilkan estimasi yang lebih akurat. Dengan menggunakan bahasa pemrograman PHP, HTML, dan SQL Server, aplikasi ini mampu mengumpulkan gejala yang dilaporkan oleh pengguna dan menganalisisnya untuk memberikan diagnosis yang lebih tepat. Tidak hanya memberikan diagnosis, aplikasi ini juga memberikan informasi tentang tingkat kepercayaan terhadap kemungkinan penyakit ginjal yang diderita oleh pengguna. Dengan memberikan informasi yang komprehensif dan akurat, aplikasi ini diharapkan dapat membantu pengguna dalam mengidentifikasi penyakit ginjal yang mungkin dialami dan memberikan informasi yang berguna untuk langkah-langkah pengobatan selanjutnya. Dengan demikian, aplikasi sistem pakar ini tidak hanya bertujuan untuk meningkatkan kesadaran akan pentingnya deteksi dini terhadap penyakit ginjal, tetapi juga untuk memberikan solusi yang konkret dan terukur dalam menangani masalah kesehatan ini.
ANALISIS PENGARUH CITRA MEREK, KUALITAS PELAYANAN, HARGA, DAN PROMOSI TERHADAP KEPUASAN KONSUMEN GRAB Rahmah, Nadya; Ritonga, Arnah; Indra, Zulfahmi
Eksakta : Jurnal Penelitian dan Pembelajaran MIPA Vol 9, No 2 (2024): Eksakta : Jurnal Penelitian dan Pembelajaran MIPA
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan, UM-Tapsel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/eksakta.v9i2.390-399

Abstract

Tujuan penelitian ini yaitu menganalisis beberapa pengaruh variabel seperti citra merek, kualitas pelayanan, harga dan promosi jasa transportasi Grab terhadap kepuasan konsumen. Data diperoleh dari total sampel 105 responden yang merupakan mahasiswa Fakultas MIPA Universitas Negeri Medan. Analisis yang dipakai adalah regresi linier berganda menggunakan bantuan software SPSS. Variabel bebas dalam riset ini adalah citra merek (X1), kualitas pelayanan (X2), harga (X3), promosi (X4) dan kepuasan konsumen (Y ). Hasil penelitian menunjukkan bahwa selain variabel X1, variabel X2,X3,X4 memberikan pengaruh terhadap kepuasan konsumen. Koefisien Determinasi (R2) adalah 52, 3% dan hasil uji F diperoleh bahwa keseluruhan variabel bebas memberikan pengaruh pada kepuasan konsumen (Y ). Hasil lack of fit test menun-jukkan bahwa model linier cocok atau sesuai untuk menjelaskan hubungan1antara variabel independen (X1, X2, X3, X4) dengan variabel Kepuasan1Konsumen (Y ).
Dynamic Programming Implementation for Delivery Route Optimization in E-Commerce Logistics Priscilia, Selfi Audy; Indra, Zulfahmi; Putri, Fahra Pebiana
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 10 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i10.6423

Abstract

The rapid growth of e-commerce has created new challenges in logistics optimization, particularly in terms of delivery route efficiency. This research develops a dynamic programming model to optimize delivery routes in the context of e-commerce in Indonesia. Using a modified Vehicle Routing Problem with Time Windows (VRPTW) approach, we implemented an algorithm that considers various factors such as distance, time, and cost. Simulations using synthetic datasets showed efficiency improvements of 18.7% in travel distance and 22.3% in delivery time compared to conventional methods. Field trials with an e-commerce partner resulted in a 21.5% reduction in travel distance and an increase in on-time delivery rate from 87% to 94%. Sensitivity analysis revealed that the algorithm's performance is most affected by demand fluctuations and changes in traffic conditions. Implementation challenges include integration with existing systems and consideration of workforce impact. This research opens avenues for further development in algorithm scalability, integration of sustainability factors, and adaptation to various geographical contexts, demonstrating significant potential for improving e-commerce logistics efficiency in the future.
Analisis Perbandingan Algoritma Penjadwalan Prioritas Preemptive dan Non-Preemptive Menggunakan Aplikasi Web Interaktif Rumahorbo, Gilbert Aldrich; Zulfahmi Indra; Alfarizi Wijaya; Melika Debiyana Putri; Buulolo, Calvin Sahputra
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 2 (2025): Oktober : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i2.994

Abstract

CPU scheduling is a core function in modern operating systems that significantly impacts system performance and efficiency. Among various scheduling algorithms, Priority Scheduling is widely used and exists in two main variants: non-preemptive and preemptive. The non-preemptive mode allows a process to run to completion, while the preemptive mode can interrupt a running process for a higher-priority one. Understanding the behavioral differences between these modes is crucial but often challenging through manual calculations. To address this, an interactive web-based application was developed to simulate and visualize both preemptive and non-preemptive Priority Scheduling algorithms. The research method involved designing the system logic based on the core principles of each scheduling variant, followed by implementation using standard web technologies: HTML, Tailwind CSS, and JavaScript. The application allows users to input custom process data or load predefined case studies, select the scheduling mode, and instantly receive a comprehensive analysis. The results include a dynamically generated Gantt chart, a detailed performance metrics table (including turnaround time and waiting time), and a step-by-step execution log. Through a comparative analysis of a specific case study, the application is proven to be an effective educational tool. It accurately simulates both modes and visually demonstrates the impact of preemption on execution order, resource utilization, and key performance metrics, thereby simplifying the learning process for students and educators.
Website-Based Employee Attendance Information System (Case Study: PT. Excelindo Karya Abadi) Batubara, Shabrina Husna; Ananta, Willy Pramudia; Indra, Zulfahmi
Journal of Computer Science Advancements Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i3.1106

Abstract

This research aims to develop a web-based employee attendance information system at PT. Excelindo Karya Abadi, overcoming inefficiencies in the manual attendance process. Using the Waterfall method, this research includes the stages of needs analysis, system design, implementation, testing, and maintenance. Data collection is carried out through interviews and direct observation so that it becomes the basis of a system that includes the function of recording attendance via selfie photos, managing employee data, and making reports. The system architecture is designed with front-end and back-end components, using technologies such as HTML, CSS, JavaScript, PHP, and MySQL. Testing involves black box techniques to ensure functionality and user feedback for system improvement. The implemented system demonstrated significant improvements in the accuracy and efficiency of attendance tracking, reducing the potential for data manipulation and errors. The transition to a web-based system allows for greater accessibility and integration with an organization's existing systems, thereby contributing to increased operational efficiency. The findings show that digital attendance systems can simplify administrative processes substantially, offer reliable solutions for employee attendance management, and align with technological advances to support company growth.
Pengembangan Model Prediksi Cuaca Hibrida Adaptif Berbasis Klasifikasi Pola dan Pembelajaran Mendalam untuk Mitigasi Bencana di Indonesia Drilanang, Mhd Ilyasyah; Indra, Zulfahmi; Walidin, Adamsyach Prana; Zai, Tri Sapta Warman
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 3 (2025): November: Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/6nagaj85

Abstract

This study aims to develop and evaluate an adaptive hybrid weather prediction model that combines pattern classification techniques with a deep learning approach to improve forecasting accuracy, especially for extreme weather events. Using a quantitative-based Research and Development (R&D) approach, this study utilizes ten years of daily rainfall time series data from the Juanda Meteorological Station. The method developed comprises three main phases: weather pattern classification using K-Means clustering to separate normal and extreme patterns; development of a specialist prediction model using SARIMA for seasonal patterns and LSTM for non-linear patterns; and integration of both models into a single adaptive framework. The results show that the adaptive hybrid model performs significantly better than the single model, with a Mean Absolute Percentage Error (MAPE) of 8.76% and a Root Mean Square Error (RMSE) of 9.13%. The main contribution of this study is the development of an intelligent, accurate prediction framework with strong potential for integration into the national early warning system, thereby supporting more effective disaster mitigation efforts in Indonesia. Further research is recommended to validate the model in various regions and add additional climate variables to improve prediction accuracy.
Co-Authors Abdi Setiawan Adidtya Perdana, Adidtya Adwitia, Keysa Shifa Agata Putri Handayani Simbolon Alfarizi Wijaya Alsya Adelia Putri Ananda Hatmi, Reza Ananda, Rizky Ananta, Willy Pramudia Anastasya Carity S, Disty Angga Warjaya ARNAH RITONGA, ARNAH Arnita Arnita Asra, Naufal Aqiilah Barus, Angelica Batubara, Shabrina Husna Buulolo, Calvin Sahputra Chairunisah Chairunisah, Chairunisah Dede Yusuf Wagiman DIdi Febrian Drilanang, Mhd Ilyasyah Evaliana Sembiring, Khatrin Farezi, Nazwar Farhan Ramadhan, Haikal Fauzan, M Rosyid Halawa, Sovantri Putra Paskah Harahap, Muhammad Abarorya Hasibuan, Muhammad Alby Savana Hasibuan, Najwa Latifah Hermawan Syahputra Hidayat, Muhammad Ferdiansyah Hijka Listia Hutagalung, Fhadillah Br Ida Ayu Putu Sri Widnyani Inna Muthmainnah Insan Taufik Kana Saputra S Kartika, Dinda Khairani, Nerli Khusnul Arifin . Lorinez, Yohana Lubis, Afiq Alghazali Lubis, Fauzan Azima Lubis, Muhammad Ghafur Rahman Luge, Miclyael M. Reza Pratama Harahap MANSUR AS Manullang, Sudianto Melika Debiyana Putri Muhammad Andika Muslim Muhammad Ridho Muhammad Rizki Alfahri Nasution, Adzkia Nur Nasution, Hamidah . Neltriana Syafira Niska, Debi Yandra Nouri, Maulana Al Palevi, Muhammad Rheza Pandiangan, Gus Rosauli Paramitha Purba, Desni Parapak, R Putri Angela Pratama, Ega Priscilia, Selfi Audy Purba, Jogi Putri, Fahra Pebiana Putri, Repi Meilani Putri, Rezkya Nadilla Rahmah, Nadya Ramayani Siagian Rinjani Cyra Nabila Risna Simorangkir Rizal Muslim Sinaga Rumahorbo, Gilbert Aldrich Sabina Wardaniah Said . Iskandar Saketang, Tia Risky Yasmin Samosir, Wahyu Ardiantito Saragih, Vinny Ramayani Savana HSB, Muhammad Alby Siagian, Angel Agasari Simamora, Elmanani Simanjorang, Rio Givent A Simanulang, Mika Monica Fransiska Simanungkalit, Ada Novisari D. Sinaga, Marlina Setia Sipahutar, Nuriana Siregar, Ary Prandika siti wulandari Sitompul, Dicky Sambora Sri Mulyana subanar subanar Sultan Lazuardiansyah SUSIANA Syahfitri, Ardilla Syarida Aini, Desti Syifa Cendikia, Yolanda Wahabi Hasibuan, Rahman Walidin, Adamsyach Prana Yulita Molliq Rangkuti Zai, Samuel Anaya Putra Zai, Tri Sapta Warman