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Jurnal Nasional Teknologi Komputer
Published by CV. HAWARI
ISSN : 28087801     EISSN : 28084845     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Nasional Teknologi Komputer di bidang ilmu komputer dan teknologi. Jurnal JNASTEK diterbitkan oleh CV. Hawari. Redaksi mengundang peneliti, praktisi, dan mahasiswa untuk menulis perkembangan ilmiah di bidang-bidang yang berkaitan dengan teknologi informasi, teknik informatika dan sistem komputer. Jurnal JNASTEK terbit 4 (Empat) kali dalam setahun pada bulan Januari, April, Juli dan Oktober. Jurnal ini berisi artikel penelitian dan kajian ilmiah.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 316 Documents
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IMPLEMENTASI METODE PROBABILITAS UNTUK ANALISIS PELUANG PENDAFTARAN MAHASISWA BARU BERDASARKAN POTONGAN DISKON DAFTAR ULANG Fery Anugerah; Boy Rizki Akbar; Chelfina Utami; Muhammad Fahriza; Laila Maghfirah; Rian Farta Wijaya; Zulham Sitorus
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i3.449

Abstract

Competition among universities to attract new students has prompted educational institutions to implement effective promotional strategies, one of which is offering re-enrollment discounts to prospective students. This study aims to analyze the likelihood of new student enrollment based on the magnitude of re-enrollment discounts using a quantitative approach and historical data on new student admissions. The analysis employs a simple probability method to measure the likelihood of prospective students re-enrolling based on the discount category offered. The results show that an increase in the discount rate leads to an increase in the likelihood of new student re-enrollment, with the highest probability—0.79, or 79%—observed in the 30% discount category. The application of this probability method has proven to assist universities in formulating promotional strategies and supporting more effective decision-making in the new student admission process.
ANALISIS DAN EVALUASI SISTEM INFORMASI POSYANDU MENGGUNAKAN METODE PIECES DALAM PENGELOLAAN DATA BALITA DI TANAH MERAH chandra syahputra; Raja Nasrul fuad; Victor Ginting
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i3.454

Abstract

Posyandu memiliki peran strategis dalam pemantauan tumbuh kembang balita dan kesehatan ibu hamil di tingkat komunitas, namun Posyandu Desa Tanah Merah masih menggunakan sistem pencatatan manual berbasis buku register dan Kartu Menuju Sehat (KMS) yang memiliki berbagai keterbatasan operasional. Tujuan: Penelitian ini bertujuan menganalisis dan mengevaluasi kinerja sistem informasi yang berjalan pada Posyandu Desa Tanah Merah menggunakan kerangka kerja PIECES (Performance, Information, Economics, Control, Efficiency, dan Service) sebagai instrumen diagnostik. Metode: Penelitian menggunakan pendekatan mixed methods yang menggabungkan analisis kuantitatif berbasis kuesioner skala Likert dan analisis kualitatif melalui wawancara mendalam. Populasi berjumlah 5 orang kader aktif dan seluruhnya dijadikan sampel menggunakan teknik total sampling, dengan hasil yang diintegrasikan melalui triangulasi. Hasil: Dimensi Performance menjadi kelemahan paling kritis dengan skor 4,00 (kategori Buruk), disusul Information dengan skor 3,00 (Cukup) dan Efficiency dengan skor 2,60 (Cukup), sementara Economics (2,00), Control (2,10), dan Service (2,30) berada pada kategori Baik. Temuan kualitatif mengungkap bahwa data fisik pernah mengalami kerusakan dan kehilangan, yang mencerminkan risiko kelangsungan data yang serius. Kesimpulan: Kelemahan paling mendesak pada sistem manual terletak pada dimensi kinerja, dan penelitian ini merekomendasikan pengembangan sistem informasi Posyandu berbasis cloud dengan fitur kalkulasi status gizi otomatis sebagai solusi jangka panjang.  
Application of Decision Tree Algorithm for Classification of Adolescent Mental Health Using RapidMiner Aidul Safii
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

Abstract−Mental health problems among adolescents have emerged as a serious concern in global scientific discourse, particularly as digital media usage intensifies. This study aims to develop a depression risk classification model for adolescents aged 13–19 years using the C4.5 (J48) Decision Tree algorithm, with predictor variables including daily social media usage duration, sleep quality and quantity, academic performance, and stress level. The data source is the Teen Mental Health Dataset from Kaggle, comprising 1,200 records with 13 attributes collected from 2022 to 2024. All analytical stages—from data preprocessing to model evaluation—were conducted using RapidMiner Studio. Model validation was performed through 10-fold cross-validation, yielding accuracy of 92.5%, precision of 91.8%, recall of 89.3%, and F1-Score of 90.5%. Based on feature importance analysis, stress_level, anxiety_level, and addiction_level proved to be the three dominant predictors of depression risk. TikTok users consistently showed higher stress levels than Instagram or combined platform users. The resulting model is interpretable and has potential for direct integration into school counseling practice and digital health research.
Perancangan Arsitektur Database Terdistribusi pada Sistem Manajemen Data Universitas Pembangunan Panca Budi Rido Favorit Saronitehe Waruwu
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

Universitas Pembangunan Panca Budi (UNPAB) Medan mengelola ekosistem akademik yang dinamis dengan populasi mahasiswa mencapai 14.508 jiwa di berbagai fakultas dan jenjang pendidikan. Tingginya lonjakan beban lalu lintas data pada periode krusial—seperti pengisian Kartu Rencana Studi (KRS), pembayaran termin, dan pendaftaran mahasiswa baru (PMB)—berpotensi menimbulkan masalah kelambatan respons (high latency) dan single point of failure (SPOF) pada basis data terpusat. Penelitian ini bertujuan merancang arsitektur basis data terdistribusi (Distributed Database Management System / DDBMS) berbasis Hybrid Cloud-On Premise yang mengintegrasikan server lokal fakultas (Linux Debian & Mikrotik) dengan infrastruktur Amazon Web Services (AWS RDS & EC2). Metode perancangan menerapkan fragmentasi horizontal berdasarkan predikat fakultas, fragmentasi vertikal pada data master mahasiswa, serta strategi replikasi data terencana. Pemrosesan transaksi diatur menggunakan protokol Two-Phase Commit (2PC) dan Saga Pattern, sedangkan optimasi kueri terdistribusi menerapkan teknik Semi-Join. Hasil pemodelan matematis menunjukkan bahwa implementasi arsitektur DDBMS ini mampu meningkatkan ketersediaan (availability) data akademik hingga 99,995% dan menekan biaya transmisi jaringan secara signifikan. Arsitektur ini memberikan solusi yang skalabel, andal, dan aman bagi efisiensi sistem manajemen data di UNPAB.
PENERAPAN DATA MINING DENGAN ALGORITMA K-MEANS CLUSTERING UNTUK MENGELOMPOKKAN TINGKAT KEMAMPUAN AKADEMIK SISWA DI SMK NEGERI 2 ULU MOROO Perianus Lombu
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

The utilization of academic data in educational institutions is often underutilized, leading to the "Data Rich, Information Poor" phenomenon. At SMK Negeri 2 Ulu Moroo, student ability evaluation still relies on a single linear average value, which contains methodological flaws as it disguises the disparity between theoretical competence (cognitive) and practical vocational skills (psychomotor). To address this issue, this research applies Educational Data Mining (EDM) techniques using the K-Means Clustering algorithm based on the CRISP-DM framework. The study involved $N = 176$ students from grades X and XI, with feature variables including Cognitive ($X_1$), Psychomotor ($X_2$), and Affective ($X_3$) scores, which were homogenized using Min-Max Normalization $[0, 1]$ to eliminate scale bias. Cluster validation using a combination of the Elbow Method and Silhouette Coefficient determined the optimal number of clusters at $k = 3$, with a WCSS variance reduction of $59.95\%$ and Silhouette score of $0.68$ (Strong Structure). Centroid denormalization partitioned student academic profiles into three categories, namely Cluster 1 or High Achievers consisting of 62 students showing linear dominance with cognitive score of $86.45$ and a psychomotor score of $89.20$, Cluster 2 or Middle Achievers with 79 students having a stable cognitive score of $74.20$ but a fluctuating psychomotor score of $78.10$, and Cluster 3 or Underachievers comprising 35 students performing below the passing grade with a cognitive score of $68.70$ and a psychomotor score of $66.40$. These findings serve as a Decision Support System the school to implement differentiated learning, targeted remedial programs, and evidence-based industrial internship placements.
SEGMENTASI PORTOFOLIO PRODUK BERBASIS PROFITABILITAS MENGGUNAKAN K-MEANS DAN ATURAN ASOSIASI UNTUK PERANCANGAN STRATEGI BUNDLING PADA KEDAI KOPI Fadhlan Ihsan Lubis
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

high-volume products do not necessarily yield proportionate profit contributions if their cost of goods sold is also high. This study aims to develop a product portfolio segmentation approach that integrates profitability dimensions into clustering and association rule mining processes. The dataset consists of 13,159 transactions and 22,185 itemized rows from Jalan Cerita Kopi & Space over a 180-day period from January to June 2026, supplemented by cost of goods sold data for all 59 products. The methodology employs K-Means clustering utilizing six derived features capturing sales volume, profit margin, total profit contribution, and purchasing behavior. Validation is performed via Elbow, Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index, and validated against Agglomerative Hierarchical Clustering. Association rule mining is executed using the FP-Growth algorithm, after which each rule is re-evaluated using a profit-based utility metric. The results yield four distinct product clusters with a Silhouette score of 0.457, a Davies-Bouldin Index of 0.873, and an agreement level of 0.883 with Agglomerative Clustering measured by Adjusted Rand Index. The primary finding reveals that association rule rankings based on lift and profit utility are virtually uncorrelated, with a Spearman correlation coefficient of only 0.168 and zero overlap among the top ten rules. The rule with the highest lift generated Rp 1,367,000 in profit, whereas the rule with the highest utility yielded Rp 3,309,000 despite a modest lift of 1.08. These findings demonstrate that bundling strategies designed solely on frequency metrics risk guiding business owners toward product combinations that are frequently purchased yet financially sub-optimal.
Klasifikasi Tingkat Demensia Alzheimer’s Berbasis Machine Learning dan Deep Learning Salwa Nur JB; Fachrurazy; Irwansyah Putera Sitorus; Katharina Tyas Aprilia
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline, particularly in memory and reasoning abilities [1]. Early detection of disease severity plays a critical role in improving clinical decision-making [4]. This study aims to classify Alzheimer’s dementia levels using a public MRI dataset from Kaggle consisting of four classes: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented [11]. This research compares conventional machine learning methods, namely Decision Tree and Random Forest, with a deep learning approach using Convolutional Neural Network (CNN) [4][8]. The experimental stages include preprocessing, data splitting, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix [4][10]. The results show that Random Forest outperforms Decision Tree with an accuracy of 89%, while CNN achieves the highest performance at 92% [2][3][4]. These findings indicate that CNN is more effective in extracting spatial features from MRI images compared to traditional machine learning methods [2][3][9].
PENERAPAN ALGORITMA RANDOM FOREST DALAM MENGKLASIFIKASIKAN KELULUSAN TEPAT WAKTU MAHASISWA PASCASARJANA TEKNOLOGI INFORMASI STAMBUK 2023 UNIVERSITAS PEMBANGUNAN PANCA BUDI Ade Surya Bakti Pane; Harmiati Bungsu Bangun; Astri Mutia Rahma; Rian Farta Wijaya
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

Timely graduation is one of the indicators of success in higher education administration. Students in the Master of Information Technology Program are required to complete all academic obligations, including fulfilling study-credit requirements and completing a thesis, within the normal study period of two years or four semesters. This study aims to apply the Random Forest algorithm to classify timely graduation among Master of Information Technology students from the 2023 cohort at Universitas Pembangunan Panca Budi, analyze the model performance, and identify the academic attributes that contribute most to the classification results. The study used academic data from 27 students, including total completed credits, Cumulative Grade Point Average (CGPA), and graduation status. Graduation status was categorized into timely graduation and delayed graduation. The data were processed through preprocessing, label encoding, an 80:20 training-testing data split using stratified split, and Random Forest model development with 100 decision trees. The results showed that the model achieved an accuracy of 83.33%, precision of 100%, recall of 80.00%, and an F1-score of 88.89%. The feature importance analysis indicated that CGPA contributed 100% to the classification results, while completed credits contributed 0% because all students completed the same number of credits, namely 48 credits. Therefore, the Random Forest algorithm can be used to classify students’ timely graduation based on the available academic data.
Rancang Bangun Smart Flasher Kendaraan Berbasis ESP-01S Fadil Hanafi; Irwan Daniel; Raja Nasrul Fuad
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i3.478

Abstract

Penelitian ini merancang bangun modul Smart Flasher kendaraan berbasis mikrokontroler ESP-01S. Tujuan utama penelitianadalah menyediakan sistem pencahayaan yang dapat dikustomisasi dan dikendalikan secara nirkabel melalui dashboard weblokal. Sistem ini mengintegrasikan antarmuka berbasis web untuk pemilihan mode, yang memungkinkan pengguna mengaturpola kedipan, kecepatan, dan efek pencahayaan secara real-time. Untuk memastikan reliabilitas, sistem menggunakanEEPROM untuk fungsi auto-save guna mencegah kehilangan konfigurasi saat daya dimatikan, serta fitur Web Over-The-Air(OTA) untuk pembaruan perangkat lunak yang efisien. Selain itu, mekanisme keamanan diimplementasikan denganmenonaktifkan modul Wi-Fi setelah 10 menit beroperasi untuk mencegah akses tidak sah. Perangkat keras dilindungi denganteknik resin potting untuk menjamin ketahanan terhadap getaran dan faktor lingkungan. Hasil pengujian menunjukkan bahwaperangkat berfungsi dengan akurat dan stabil dengan antarmuka web yang intuitif. P
PERANCANGAN WEBSITE MULTIMEDIA INTERAKTIF SEBAGAI MEDIA PROMOSI DAN INFORMASI DI WARUNG KOP I JURNALIS MEDAN Fauzul Zikri; Ricky Ramadhan harahap; Rahmadani
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i3.480

Abstract

The development of information and communication technology encourages business actors to utilize digital media as an effective means of promotion and information delivery. Warkop Jurnalis Medan, as a local culinary business, still relies on conventional promotional media, which limits the reach of information to consumers. This study aims to design an interactive multimedia website that functions as a promotional and information medium for Warkop Jurnalis Medan. The research method used is the System Development Life Cycle (SDLC) method, which includes the stages of requirements analysis, design, implementation, and testing. The website is designed by integrating multimedia elements such as text, images, video, and interactive animation to enhance user engagement. The results of this study indicate that the interactive multimedia website is capable of presenting information on menus, prices, locations, and the coffee shop's atmosphere in a more attractive and easily accessible manner for the public. With this website, it is expected to increase promotional effectiveness, expand marketing reach, and boost consumer interest in visiting Warkop Jurnalis Medan.

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