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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JITU : Journal Informatic Technology And Communication
Ghoni, Umar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Journal : JITU : Journal Informatic Technology And Communication

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Prototype Palang Pintu Kereta Api Otomatis Berbasis IoT Yuniarti Lestari; Ghoni, Umar; Rimandita, Agung
JITU Vol 9 No 1 (2025)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v9i1.1805

Abstract

Kereta api sering melewati pemukiman dan jalan raya, sehingga diperlukan palang pintu sebagai tanda bagi pengendara dan pejalan kaki untuk berhenti saat kereta melintas. Namun, banyak palang pintu yang masih beroperasi secara manual, meningkatkan risiko kecelakaan akibat kelalaian operator atau ketidaksabaran pengguna jalan. Untuk mengatasi masalah ini, diperlukan sistem otomatisasi palang pintu kereta api berbasis IoT yang dapat dikendalikan dari jarak jauh, guna meningkatkan keselamatan dan efisiensi. Prototype palang pintu kereta api otomatis dalam penelitian ini memanfaatkan ESP32, sensor infrared, motor servo, dan aplikasi Telegram. Pengembangan sistem mengikuti metode Extreme Programming yang terdiri dari empat tahapan: perencanaan, desain, pemrograman, dan pengujian. Hasil pengujian dalam penelitian ini menunjukkan bahwa sensor infrared dapat mendeteksi objek dari jarak 1 hingga 7 cm dan bekerja dengan sudut deteksi antara 10 hingga 100 derajat. Ketika objek mainan kereta api didekatkan ke sensor, LED indikator menyala merah, menandakan adanya objek, dan servo langsung bergerak untuk menutup palang kereta api hingga 100 derajat. Setelah 5 detik, servo kembali ke posisi awal 10 derajat, dan LED berubah menjadi kuning, menandakan bahwa objek sudah tidak ada.
Optimizing Clustering Performance: A Novel Integration of Whale Optimization Algorithm and K-NN Validation in Data Mining Analytics Nur Wahyu Hidayat; Umar Ghoni; Mursalim
JITU Vol 9 No 1 (2025)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v9i1.1808

Abstract

The digital era's massive data necessitates effective clustering, a machine learning technique grouping data by similarity. Clustering large, complex datasets faces challenges like volume, dimensionality, and variability, hindering algorithms like K-Means. A key issue in K-Means is its sensitivity to initial centroid selection, impacting results. This research aims to optimize clustering performance by integrating the Whale Optimization Algorithm (WOA) for improved initial centroid determination in K-Means, and K-Nearest Neighbors (K-NN) for validating the resulting cluster quality through classification accuracy. Evaluation on iris, wine, heart, lung, and liver datasets using the Davies-Bouldin Index (DBI) showed that WOA-KMeans consistently yielded lower DBI values compared to standard K-Means, indicating superior clustering. Notably, DBI for the lung dataset drastically decreased from 2.38016 to 0.65395. Furthermore, K-NN classification using the generated cluster labels achieved high accuracy (98-99% across datasets), confirming well-separated and internally homogeneous clusters. This demonstrates WOA's effectiveness in guiding K-Means towards better solutions and K-NN's utility in validating cluster distinctiveness. This novel WOA-K-NN combination offers a more accurate and robust clustering method. The significant performance improvements observed across diverse datasets highlight its potential for enhanced data exploration and pattern discovery in complex data mining tasks.
Co-Authors Ikhwan Anshori Khairina Eka Setya Putri Lestari, Yuniarti Mursalim Mursalim Nur Hidayat, Wahyu Nur Wahyu Hidayat Rakhmawati, Hidayatur Rimandita, Agung