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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal Software, Hardware and Information Technology Jurnal Komtekinfo JT-IBSI (Jurnal Teknik Ibnu Sina) Jurnal Informatika Polinema (JIP)
Rifaldi Herikson
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Implementasi Machine Learning untuk Klasifikasi Buku Otomatis pada Perpustakaan Digital SLAM, Berta Erwin; Feri Irawan; Nolan Efranda; Rifaldi Herikson
Jurnal Informatika Polinema Vol. 11 No. 3 (2025): Vol. 11 No. 3 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v11i3.7298

Abstract

Permasalahan klasifikasi buku dalam sistem perpustakaan digital, khususnya di tingkat sekolah menengah atas (SMA), masih menjadi tantangan karena banyak institusi belum mengadopsi sistem klasifikasi otomatis. Proses manual dinilai tidak efisien dan rawan inkonsistensi. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis berbasis machine learning menggunakan algoritma Naïve Bayes, yang dikenal efektif dalam pengolahan teks. Data yang digunakan terdiri dari 10.000 entri buku digital, yang masing-masing mencakup metadata berupa judul, sinopsis, dan kata kunci. Proses preprocessing dilakukan melalui normalisasi teks, penghapusan stopword bahasa Indonesia, serta transformasi ke dalam representasi vektor menggunakan metode TF-IDF. Model dilatih untuk mengenali sepuluh kategori utama dengan berbagai rasio pembagian data latih dan uji, mulai dari 90:10 hingga 50:50. Hasil evaluasi menunjukkan bahwa model mampu menghasilkan akurasi tinggi di berbagai skenario, dengan rentang akurasi antara 89,2% hingga 90,3%. Menariknya, performa model justru meningkat secara konsisten seiring meningkatnya proporsi data uji. Precision dan recall makro juga menunjukkan tren serupa, yang menandakan bahwa model Naïve Bayes cukup robust bahkan saat data latih terbatas. Secara keseluruhan, sistem ini terbukti efektif dalam meningkatkan efisiensi dan konsistensi klasifikasi koleksi perpustakaan digital. Temuan ini merekomendasikan integrasi sistem klasifikasi otomatis ke dalam platform perpustakaan SMA, serta membuka peluang eksplorasi algoritma lanjutan dan pengembangan fitur rekomendasi cerdas di masa depan.
Design of Web-Based Agricultural Product Marketing System in Toapaya Village Rifaldi Herikson; Berta Erwin SLAM; Feri Irawan; Nolan Efranda
Jurnal KomtekInfo Vol. 12 No. 2 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i2.643

Abstract

Marketing agricultural products remains a major challenge for farmers in Toapaya Village, located in Bintan Regency. The selling prices received by farmers are often significantly lower than the market prices paid by end consumers. This is primarily due to the lengthy distribution chain and dependence on middlemen who take a large portion of the profits. Limited market access further weakens the farmers' position in setting prices and marketing their harvest independently. To address this issue, a web-based marketing system is proposed to facilitate direct transactions between farmers and customers, aiming to shorten the distribution chain and increase farmers' income. The system was developed using the Waterfall software development methodology to support a structured, user-oriented development process, while system modeling was carried out using UML to provide a detailed overview of the system flow. The research results indicate that the developed web-based marketing system can simplify the agricultural marketing process and expand market access. System evaluation will be conducted periodically through direct testing to collect feedback from users. Based on testing results, the system has been proven to enhance product accessibility and facilitate online transactions. Therefore, this marketing system is expected to contribute to economic growth, improve farmers' welfare, and serve as a form of digital transformation in the agricultural sector in Toapaya Village, Bintan Regency.
Scenario-Based Association Rule Mining in Veterinary Services Using FP-Growth: Differentiating Clinical and Customer-Driven Patterns Rafi Dio; Aulia Agung Dermawan; Dwila Sempi Yusiani; Rifaldi Herikson; Andikha, Andikha; Dwi Ely Kurniawan; Adyk Marga Raharja
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9698

Abstract

Veterinary clinics routinely generate transactional data that contain valuable information about both operational workflows and customer preferences. This study aims to differentiate between procedural and customer-driven service patterns by applying the FP-Growth association rule mining algorithm to 1,000 anonymized transactions comprising 94 unique items, collected from a veterinary clinic in West Java, Indonesia, during 2023. Two distinct analytical scenarios were constructed: Scenario 1 includes all services (procedural and customer-driven), while Scenario 2 excludes procedural items such as “Vet” and “Visit Dokter” to focus solely on client-initiated behaviors. Data preprocessing involved aggregating transaction items into a market basket format suitable for frequent pattern mining. The FP-Growth algorithm was employed to extract association rules, evaluated using support, confidence, and lift metrics. Results from Scenario 1 revealed rule patterns reflective of standard clinical protocols and operational dependencies, informing bundled service packages and inventory management. In contrast, Scenario 2 uncovered customer-driven associations, highlighting opportunities for personalized promotions and service innovation. The comparative analysis demonstrates the utility of scenario-based association rule mining for both operational optimization and customer engagement. While the findings provide actionable insights for clinic management, further validation with practitioners and implementation in multi-clinic settings are recommended to confirm real-world applicability and enhance generalizability.
Segmentasi Pelanggan Klinik Dokter Hewan Berbasis Algoritma K-Means dan Model RFM Rafi Dio; Meylia Vivi Putri; Dwila Sempi Yusiani; Andikha; Berta Erwin SLAM; Adyk Marga Raharja; Rifaldi Herikson
Jurnal Teknik Ibnu Sina (JT-IBSI) Vol. 10 No. 1 (2025): JT-IBSI (Jurnal Teknik Ibnu Sina)
Publisher : Fakultas Teknik Universitas Ibnu Sina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jt-ibsi.v10i1.1180

Abstract

This study aims to segment customers of a veterinary clinic using a combination of Recency, Frequency, Monetary (RFM) analysis and the K-Means clustering algorithm. Transactional data from December 2024 to April 2025 were processed to generate key customer features, followed by min-max normalization to ensure comparability across variables. The segmentation was conducted in AI Studio 2025, with cluster quality evaluated using cluster distance performance and Davies-Bouldin Index. The analysis resulted in four distinct customer segments: the majority were passive customers with low transaction frequency and spending, while a smaller group showed high purchasing activity and made a significant economic contribution. This study demonstrates the effectiveness of automated data mining tools in uncovering meaningful customer profiles in a veterinary service context. The results provide a practical basis for targeted marketing, customer retention strategies, and service improvement in veterinary clinics. This approach offers valuable insights for data-driven decision making and represents a novelty for veterinary service management in Indonesia. Keywords: Customer Segmentation, RFM Analysis, K-Means Clustering, Veterinary Clinic, Data Mining.
Pengujian Sistem Identitas Digital Siswa Berbasis Blockchain untuk Keamanan dan Transparansi Menggunakan Black-Box Testing Slam, Berta Erwin; Irawan, Feri; Efranda, Nolan; Herikson , Rifaldi
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.182

Abstract

This study proposes a blockchain-based student identity card system as a secure and transparent solution for managing digital identities at the senior high school level. The system is designed to address various issues found in conventional identification methods, such as data forgery, physical card damage, and limited verification capabilities. Developed using the Laravel framework and integrated with a private blockchain network, student identity data is hashed and stored permanently. The digital identity is represented as a QR code printed on the student ID card. Identity verification can be performed independently through a web-based application without relying on a central authority. System evaluation was conducted through functional testing using the black-box testing method, as well as user testing to assess reliability, efficiency, and usability. Test results showed that the system was able to verify identities with an average QR code response time of less than 3 seconds and a login success rate of 98%. Therefore, the integration of blockchain technology into the web platform proves to be an innovative and feasible approach to modernizing student identity management. This system contributes to strengthening the digital transformation of secondary education in a secure, efficient, and decentralized manner.
Co-Authors Andikha Andikha, Andikha Aulia Agung Dermawan Dwi Ely Kurniawan Dwila Sempi Yusiani Efranda, Nolan Feri irawan Marga Raharja, Adyk Meylia Vivi Putri Rafi Dio SLAM, Berta Erwin