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All Journal REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Embedded Systems, Security and Intelligent Systems semanTIK
Audy Aldrin Kenap
Universitas Negeri Manado
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education

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Aplikasi Buku Tamu Berbasis Web Menggunakan QR Code Yustin Sengga; Audy Aldrin Kenap; Glenn D.P Maramis
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 10 No. 1 (2026): Volume 10 Nomor 1 Januari 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v10i1.15661

Abstract

Kemajuan teknologi informasi telah membawa perubahan signifikan dalam pengelolaan administrasi dan layanan publik, termasuk pengelolaan data kunjungan tamu di instansi pemerintah. Badan Pusat Statistik (BPS) Kota Manado masih menggunakan sistem pencatatan manual yang berpotensi menimbulkan kesalahan input, ketidakefisienan, dan kesulitan dalam proses pelaporan serta pengarsipan data. Penelitian ini bertujuan untuk merancang dan membangun aplikasi buku tamu berbasis web menggunakan QR Code guna meningkatkan akurasi, efisiensi, dan keamanan data kunjungan tamu. Pengembangan sistem menggunakan metode waterfall, yang terdiri dari tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian menggunakan metode Blackbox. Aplikasi memungkinkan tamu melakukan registrasi otomatis melalui pemindaian QR Code, sehingga data kunjungan tersimpan langsung ke dalam basis data tanpa input manual. Hasil implementasi menunjukkan bahwa sistem mampu mempercepat proses pendaftaran tamu, mengurangi kesalahan input, serta mendukung pengelolaan dan pelaporan data kunjungan secara digital melalui fitur ekspor Excel. Sistem ini dinilai efektif dalam menggantikan pencatatan manual dan mendukung digitalisasi administrasi di BPS Kota Manado. Aplikasi ini juga berpotensi dikembangkan lebih lanjut dengan integrasi sistem dan peningkatan fitur keamanan data.
Text Classification of 2024 Regional Head Elections Logistics Distribution in Online News Using Support Vector Machine Murni Kassa; Irene Realyta Haldy Trosi Tangkawarow; Audy Aldrin Kenap
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2619

Abstract

Purpose – This study aims to classify online news related to logistics distribution issues in the 2024 Indonesian Regional Head Elections using Support Vector Machine and imbalance handling strategies. Design/methods/approach – A total of 1,355 online news articles were collected from nine national news portals through web scraping. The research workflow involved data preprocessing, rule-based weak supervision, manual validation, TF–IDF feature extraction, oversampling using SMOTE and ADASYN, class-weighted learning, and SVM classification with Linear, RBF, Polynomial, and Sigmoid kernels. Model performance was evaluated using macro-averaged F1-score, 5-fold cross-validation, classification report, and confusion matrix analysis. Findings - The results show that Linear and RBF kernels produced more consistent performance than Polynomial and Sigmoid kernels for sparse TF–IDF representations. The RBF kernel with class-weighted learning achieved the highest hold-out macro F1-score of 0.641, although cross-validation results showed only marginal differences among top-performing configurations. The model performed well in classifying “No Issues” and “Damaged” categories but still struggled with the minority “Late” class. Research implications/limitations – The findings indicate that machine learning can support preliminary election logistics monitoring, but the model should not yet be used as a fully automated early-warning system due to minority-class limitations and weak-labeling constraints. Originality/value – This study contributes empirical evidence on SVM-based imbalanced text classification for election logistics news monitoring in the Indonesian Pilkada context.
Penerapan Algoritma Decision Tree C4.5 Pada Layanan Perbaikan Handphone dan Laptop Mesiasi Anjelika Supit; Audy Aldrin Kenap; Glenn David Paulus Maramis
SemanTIK : Teknik Informasi Vol. 12 No. 1 (2026): Volume 12 Number 1 (January-june 2026)
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v12i1.258

Abstract

Perkembangan teknologi informasi telah mendorong akan kebutuhan sistem prediksi biaya perbaikan perangkat elektronik yang cepat dan akurat. Tujuan dari penelitian ini untuk menerapkan algoritma Decision Tree C4.5 pada layanan perbaikan Handphone dan Laptop yang ditujukan bagi pelanggan A+ Service Center Manado. Data yang digunakan mencakup atribut merek perangkat, tipe unit, dan jenis kerusakan, dengan hasil berupa estimasi biaya perbaikan. Metode penelitian mencakup tahap pengumpulan data, preprocessing, penerapan algoritma C4.5, pembentukan model pohon keputusan, serta evaluasi akurasi menggunakan confusion matrix dan Rapid Miner. Hasilnya menunjukkan bahwa algoritma Decision Tree C4.5 dapat menghasilkan model dengan tingkat akurasi sebesar 81,06%, di mana atribut kerusakan menjadi faktor paling utama dalam menentukan estimasi biaya. Implementasi sistem berbasis web memberikan penghematan waktu, karena pelanggan dapat mengestimasi biaya perbaikan tanpa perlu menunggu pemeriksaan teknisi di tempat servis. Penerapan algoritma ini dapat membantu meningkatkan efisiensi layanan dan kepercayaan pelanggan terhadap proses perbaikan perangkat. The development of information technology has driven the need for a fast and accurate system to predict the repair costs of electronic devices. This study aims to apply the Decision Tree C4.5 algorithm to smartphone and laptop repair services for customers of A+ Service Center Manado. The dataset includes attributes such as device brand, unit type, and type of damage, with the output being the estimated repair cost. The research methodology involves several stages, including data collection, preprocessing, application of the C4.5 algorithm, decision tree model construction, and accuracy evaluation using a confusion matrix. The results show that the Decision Tree C4.5 algorithm can produce a model with an accuracy rate of 81.06%, where the damage type attribute is the most dominant factor in determining the co-st estimation. The implementation of the web-based system provides time efficiency, allowing customers to estimate repair costs without waiting for on-site technician inspection. The application of this algorithm helps improve service efficiency and enhances customer trust in the repair process.
Perbandingan Clustering Berbasis RFM dan Implementasi K-Means untuk Segementasi Pelanggan Bisnis Laundry Nibertin Zai; Audy Aldrin Kenap; Alfiansyah Hasibuan
SemanTIK : Teknik Informasi Vol. 12 No. 1 (2026): Volume 12 Number 1 (January-june 2026)
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v12i1.275

Abstract

Bisnis laundry seperti Anty Laundry masih mengelola program loyalitas secara manual hanya mengandalkan frekuensi transaksi untuk menentukan pelanggan prioritas tanpa mempertimbangkan kebaruan dan nilai transaksi sehingga strategi pemasaran menjadi tidak tepat sasaran dan potensi retensi pelanggan bernilai tinggi tidak optimal. Penelitian ini membandingkan tiga metode clustering (K-Means, K-Medoids, dan Hierarchical Clustering) untuk segmentasi pelanggan prioritas berbasis analisis Recency, Frequency, dan Monetary (RFM), sekaligus mengimplementasikan metode terbaik dalam sistem informasi berbasis web. Data penelitian terdiri dari 2.549 transaksi valid dari 203 pelanggan unik periode Oktober 2024–Oktober 2025. StandardScaler digunakan untuk normalisasi data dan metode elbow menentukan jumlah cluster optimal (k=5). Evaluasi menggunakan Silhouette Score, Davies-Bouldin Index, dan Calinski-Harabasz Index menunjukkan K-Means mencapai hasil terbaik dengan nilai 0.5440, 0.5005, dan 282.18, mengungguli K-Medoids (0.4790, 0.7034, 145.52) dan Hierarchical Clustering (0.5141, 0.5239, 251.73). Lima segmen pelanggan teridentifikasi: Inactive Customer (36.95%), Regular Customer (49.75%), High Value Customer (11.82%), VIP Customer (0.99%), dan Top Spender (0.49%). K-Means diimplementasikan menggunakan Streamlit dengan segmentasi otomatis dan kemampuan ekspor untuk mendukung strategi pemasaran tepat sasaran per segmen. Laundry businesses such as Anty Laundry still manage loyalty programs manually — relying solely on transaction frequency to determine priority customers without considering recency and monetary value — resulting in poorly targeted marketing strategies and suboptimal retention of high-value customers. This study compares three clustering methods (K-Means, K-Medoids, and Hierarchical Clustering) for priority customer segmentation based on Recency, Frequency, and Monetary (RFM) analysis, while implementing the best-performing method in a web-based information system. The dataset consisted of 2,549 valid transactions from 203 unique customers covering October 2024 to October 2025. StandardScaler was applied for data normalization and the elbow method determined the optimal cluster number (k=5). Evaluation using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index showed K-Means achieved the best results with values of 0.5440, 0.5005, and 282.18, outperforming K-Medoids (0.4790, 0.7034, 145.52) and Hierarchical Clustering (0.5141, 0.5239, 251.73). Five customer segments were identified: Inactive Customers (36.95%), Regular Customers (49.75%), High Value Customers (11.82%), VIP Customers (0.99%), and Top Spenders (0.49%). K-Means was implemented using Streamlit with automatic segmentation and export capabilities to support targeted marketing strategies for each segment.
Co-Authors Alfiansyah Hasibuan Glenn D.P Maramis Glenn David Paulus Maramis Irene Realyta Haldy Trosi Tangkawarow Mesiasi Anjelika Supit Murni Kassa Nibertin Zai Yustin Sengga