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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) JURNAL ILMIAH INFOKOM
Johan
Universitas Potensi Utama
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Optimasi Strategi Promosi Sekolah SMK melalui Segmentasi Data Siswa Baru dengan Clustering Metode K-Means menggunakan Differential Evolution (DE) Pebruarianto Hutabarat; Adil Setiawan; Bill Raj; M Prasetyo; M. Agung Irnanda; Empiter Gea; Johan; Andreas Parapat
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.779

Abstract

SMK XYZ faces challenges in developing effective and efficient promotional strategies to attract prospective new students. Previously, promotional approaches have been general and failed to address the specific needs of different prospective student segments. This research aims to optimize school promotional strategies by analyzing patterns in new student characteristics through data segmentation techniques. The proposed method is K-Means Clustering optimized with the Differential Evolution (DE) algorithm. DE optimization addresses K-Means' sensitivity to initial cluster center initialization, aiming for more stable and optimal segmentation. The data used includes demographic attributes, major interests, registration pathways, and prior school origins of new students from the 2023/2024 cohort. Research results show that the DE-K-Means combination produces more compact clusters (lower within-cluster sum of squares values) compared to standard K-Means. Based on the resulting cluster analysis, three distinct promotional strategies are formulated for each prospective student segment: digital-intensive approaches, partnerships with feeder schools, and highlighting specific major advantages. Implementing these strategies is expected to significantly increase the quality and quantity of new student admissions.
Kombinasi K-Means dan Fuzzy C-Means untuk Clustering Transaksi PPOB Berdasarkan Validitas Cluster Nanda Setiawan; Heru Fredi; Bualazatulo Laia; Yiska Dayanti Zagoto; Johan; Andreas Jorghy Parapat; Wahyu Saptha Negoro
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.839

Abstract

Perkembangan layanan Payment Point Online Bank (PPOB) telah mendorong peningkatan signifikan pada volume dan kompleksitas data transaksi digital yang dihasilkan. Algoritma K-Means merupakan salah satu metode clustering yang paling banyak digunakan karena kesederhanaan, efisiensi komputasi, dan kemampuannya dalam menangani data berskala besar. Tujuan penelitian adalah mengelompokkan data transaksi PPOB secara optimal menggunakan kombinasi algoritma K-Means dan Fuzzy C-Means (FCM) serta mengevaluasi kualitas cluster berdasarkan validitas cluster. Data yang digunakan dalam penelitian ini merupakan data transaksi Payment Point Online Bank (PPOB) periode Januari 2024 yang diolah menggunakan Google Colaboratory (Google Colab). Data tersimpan dalam format CSV dan berisi informasi transaksi yang dilakukan oleh berbagai loket PPOB dengan jumlah data: 498.853 data transaksi. Penerapan metode Fuzzy C-Means memberikan hasil yang lebih sesuai karena mampu merepresentasikan derajat keanggotaan ganda pada loket-loket yang berada di zona transisi antar cluster. Keberadaan zona transisi tersebut membuktikan bahwa pendekatan Fuzzy lebih tepat digunakan dalam konteks bisnis PPOB yang dinamis, di mana performa loket dapat berubah seiring waktu dan tidak selalu berada pada kategori yang bersifat mutlak.
OPTIMASI PIPELINE KLASIFIKASI PENYAKIT PADI MENGGUNAKAN STRATEGI AUGMENTASI CITRA INTENSIF DAN TRANSFER LEARNING EFFICIENTNET-B0 Dwi Robiul R; Diyo Mollana F; Nanda S; Johan; Rahmad S; Satriyo W; Rika Rosnelly
INFOKOM (Informatika & Komputer) Vol 13 No 2 (2025): JURNAL INFOKOM DESEMBER 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i2.2355

Abstract

Rice plant diseases represent a significant challenge to global food productivity. This study aims to optimize a rice disease classification pipeline using the EfficientNet-B0 architecture combined with intensive image augmentation strategies and transfer learning. The dataset comprises 10,407 rice leaf images categorized into 10 classes, including healthy conditions and nine types of diseases. Augmentation strategies such as random rotation, color jittering, and random resized cropping were implemented to enhance model robustness against diverse field conditions. Evaluation results demonstrate that the model achieved outstanding performance, with a Top-1 Accuracy of 96.25% and a Top-5 Accuracy of 99.90%. Grad-CAM++ analysis validated that the model accurately focuses feature extraction on pathological leaf areas. t-SNE visualization revealed clear feature cluster separation between classes, further supported by ROC curve AUC values reaching 1.00 for the majority of categories. This research confirms that the proposed pipeline is highly reliable for early rice disease detection and holds significant potential for mobile device implementation to assist farmers.
Co-Authors Andreas Jorghy Parapat Andreas Parapat Bill Raj Bualazatulo Laia Diyo Mollana F Dwi Robiul R Empiter Gea Heru Fredi M Prasetyo M. Agung Irnanda Nanda S Nanda Setiawan Pebruarianto Hutabarat Rahmad S Rika Rosnelly Satriyo W Setiawan, Adil Wahyu Saptha Negoro Yiska Dayanti Zagoto