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Studi Pembeban Transformator Distribusi Pada PT. PLN (Persero) Rayon Sungguminahasa Wildan Wildan; Rahmat Rahmat; Hendy Prasetyo; Sulfikar Sulfikar
EEICT (Electric, Electronic, Instrumentation, Control, Telecommunication) Vol 9, No 1 (2026)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/eeict.v9i1.23092

Jenis Penelitian ini merupakan jenis penelitian deskriptif kuantitatif yaitu memberikan gambaran dan menganalisis tentang pembebanan transformator distribusi pada PT. PLN (Persero) Rayon Sungguminasa. Pada kondisi ideal, transformator tiga fasa memiliki besaran yang sama pada setiap fasanya; perbedaannya hanya terletak pada sudut fasa yang seharusnya berjarak 120°. Namun, dalam praktiknya kondisi ideal ini sulit tercapai karena masing-masing fasa pada sisi sekunder umumnya menyalurkan daya ke beban yang berbeda-beda. Akibatnya, terjadi ketidakseimbangan beban pada setiap fasa. Hasil analisa data yang diperoleh menunjukkan bahwa pembebanan pada transformator distribusi di wilayah kerja PT. PLN (Persero) Rayon Sungguminasa masih menunjukkan adanya beberapa transformator distribusi yang melebih batas maksimum 80%, dan untuk hasil analisa data ketidakseimbangan beban juga menunjukkan adanya beberapa transformator distribusi yang melebihi batas maksimum 25%.

Implementation of the K-Means Clustering Algorithm to Identify Student Discipline Patterns Based on Attendance Data Hermila A.; Haeriani H; Wildan
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.652

Purpose – Student attendance data in vocational schools are often collected digitally but remain underutilized for managerial decision-making. This study aims to apply the K-Means Clustering algorithm to identify student discipline patterns based on attendance behavior and support objective, data-driven intervention planning. Methods – This study employed a quantitative computational experiment using attendance data from 23 students at SMK Negeri 1 Bolango Utara over 33 effective school days. Student identities were anonymized using subject codes. Two variables were analyzed: on-time attendance frequency and tardiness frequency. The number of clusters was set to K=3 based on managerial discipline categories and validated using the Elbow Method through Within-Cluster Sum of Squares analysis. Findings – The results classified students into three discipline profiles. Cluster 1 consisted of 13 students categorized as highly disciplined, Cluster 3 consisted of 5 students categorized as moderately disciplined, and Cluster 2 consisted of 5 students categorized as less disciplined. The less disciplined cluster showed a critical pattern, with an average tardiness frequency of 16.80, exceeding its average on-time attendance frequency of 16.20. Research implications – The findings indicate that K-Means clustering can transform passive attendance records into actionable discipline profiles. However, the study was limited to one school, 23 students, and two attendance variables. Originality – This study contributes a simple computational framework for developing an attendance-based early warning system for student discipline management in vocational education.

A Inorganic Waste Management as a Medium for Environmental Education and Student Creativity Development Sunardi; Jumiati Ilham; Didiet Haryadi Hakim; Sitti Suhada; Wildan; Hasmah
Vokatek: Jurnal Pengabdian Masyarakat Volume 4: Issue 1 (April 2026)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/vokatekjpm.v4i1.936

The problem of inorganic waste in school environments requires an educational approach that not only emphasizes knowledge but also provides practical experiences that foster students’ creativity and environmental awareness. This community service program aimed to strengthen students’ understanding of inorganic waste management and provide direct experience in transforming used materials into useful creative products. The activity was conducted at SD Negeri 1 Suwawa Selatan, Bone Bolango Regency, involving 30 fourth- and fifth-grade students. The implementation method consisted of preparation, interactive socialization, demonstration, group-based recycling practice, and descriptive evaluation through participation observation, product assessment rubrics, field notes, and documentation. The results showed positive achievements in four main aspects: students’ active involvement in discussions and practice, their ability to identify organic and inorganic waste, their initial understanding of the 3R principles (reduce, reuse, recycle), and their ability to produce simple recycled products such as flower pots, pencil holders, and photo frames from used materials. The activity also promoted teamwork, responsibility in completing products, and awareness of maintaining school cleanliness. Therefore, inorganic waste management can be used as a concrete, low-cost, applicable, and easily replicable medium for environmental education to support character education and the development of elementary school students’ creativity.

Perancangan Aplikasi Layanan Desa Cerdas Berbasis AI Terintegrasi WhatsApp untuk Klasifikasi Laporan Warga di Desa Hutadaa Abdul Gani Fadhlulrahman S. H. lihawa; Hendy Prasetyo; Syahrir Abdussamad; Rahmad Hidayat Dongka; Wildan Wildan; Ade Irawaty Tolago; Yasin Mohamad; Ulfatun Nadifa; Rahmatia Alam
Empiris Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 1 (2026): April 2026
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59713/kdytt710

The community reporting system in Hutadaa Village, West Limboto District, remains dependent on manual mechanisms, leading to delayed responses, unstructured documentation, and difficulty for village officials in prioritizing issues. This community service activity aims to design a prototype of an AI-based smart village service application integrated with the WhatsApp platform, enabling automated classification of community reports and assessment of urgency levels. The design was carried out through four stages: field interview-based needs identification, system architecture design, workflow simulation, and limited testing with village officials. The system was designed by integrating WhatsApp Business API, an AI-based image processing module, and a report management interface for village officials. Simulations demonstrated that the system can classify reports into categories (infrastructure, sanitation, social, emergency) and assign urgency levels (low, medium, high) based on analysis of photo and text content, achieving a category classification accuracy of 82.5% and urgency assessment accuracy of 77.5% under good image quality conditions. Interface testing by village officials yielded positive responses regarding ease of use and feature relevance. The designed system has the potential to improve the efficiency of public services at the village level. Full implementation and long-term impact evaluation are required in subsequent service activities. Sistem pelaporan masyarakat di Desa Hutadaa, Kecamatan Limboto Barat, masih bergantung pada mekanisme manual yang menyebabkan keterlambatan penanganan, dokumen laporan yang tidak terstruktur, dan kesulitan bagi aparat desa dalam menetapkan prioritas penanganan. Kegiatan pengabdian ini bertujuan merancang prototipe aplikasi layanan desa cerdas berbasis kecerdasan buatan (AI) yang terintegrasi dengan platform WhatsApp, yang memungkinkan klasifikasi otomatis laporan warga beserta penilaian tingkat urgensinya. Perancangan dilaksanakan melalui empat tahap: identifikasi kebutuhan berbasis wawancara lapangan, desain arsitektur sistem, simulasi alur kerja, dan uji coba terbatas bersama perangkat desa. Sistem dirancang dengan mengintegrasikan WhatsApp Business API, modul pemrosesan gambar berbasis AI, dan antarmuka manajemen laporan untuk aparat desa. Simulasi menunjukkan bahwa sistem mampu mengklasifikasikan laporan ke dalam kategori (infrastruktur, kebersihan, sosial, darurat) dan menetapkan tingkat urgensi (rendah, sedang, tinggi) berdasarkan analisis konten foto dan teks, dengan akurasi klasifikasi kategori sebesar 82,5% dan akurasi penilaian urgensi sebesar 77,5% pada kondisi gambar berkualitas baik. Pengujian antarmuka oleh perangkat desa menghasilkan respons positif terhadap kemudahan penggunaan dan relevansi fitur. Sistem yang dirancang berpotensi meningkatkan efisiensi layanan publik di tingkat desa. Diperlukan implementasi penuh dan evaluasi dampak jangka panjang pada tahap pengabdian berikutnya.

Comparing K-Means and Fuzzy C-Means for Student Academic Risk Mapping and Early Warning in a Basic Mathematics Course Hendy Prasetyo; Wildan; Afifah Farhanah Akadji; Andi Sitti Dwi Auliyani; Rahmad Hidayat Dongka
Jurnal MEKOM (Media Komunikasi Pendidikan Kejuruan) Volume 13, Issue 1, February 2026
Publisher : Fakultas Teknik, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/mekom.v13i1.261

Purpose – This study compares the K-Means and Fuzzy C-Means (FCM) algorithms for mapping student academic risk using in-course academic performance data. Methods – The dataset consisted of 35 students and included assignment scores, quiz scores, midterm examination scores, attendance, learning participation, employment status, and language variables. The data were preprocessed through cleaning, identity anonymization, and min-max normalization to ensure that all attributes were measured on a comparable scale. The experiments were conducted under two clustering scenarios, namely K=2 and K=3. Findings – In the K=2 scenario, both methods produced the same separation between low-risk and high-risk student groups. After the clustering results were mapped to the actual Pass/Fail labels using a majority-vote approach, 27 students who passed and 7 students who failed were correctly identified, with no false positives and 1 false negative. These results yielded 97.14% accuracy, 100% precision, 96.43% recall, and a 98.18% F1-score. In the K=3 scenario, K-Means formed three distinct groups containing 27, 4, and 4 students, whereas FCM produced a more gradual distribution of 13, 14, and 8 students. Research implications – These findings indicate that K-Means is suitable as a fast baseline for binary risk screening, whereas FCM is more informative for gradual risk interpretation in academic early warning systems. Originality – This study contributes by showing the different practical value of hard and soft clustering for identifying clearly at-risk and borderline students using routinely available in-course academic indicators.

Sosialisasi Cyber Security Awareness: Mitigasi Phishing dan Pengamanan Akun Digital bagi Siswa SMA/SMK Hendy Prasetyo; Kameliani Kameliani; Wildan Wildan; Andi Rasya; Argiyo Prasetyo Tutu
Empiris Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 1 (2026): April 2026
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59713/fh9by653

The widespread internet access in Indonesia has positioned senior high school and vocational students as the most active digital users while simultaneously making them the most vulnerable group to phishing attacks and digital account hijacking. The gap between technical proficiency and digital security awareness among adolescents has become an urgent issue requiring structured intervention. This community service activity aimed to enhance the cyber security awareness of students at SMAN 1 Limboto, Gorontalo Regency, through socialization focusing on three main aspects: phishing identification and mitigation, digital account security through Two-Factor Authentication (2FA), and data privacy principles in daily online activities. The activity employed an interactive lecture method based on real-life case studies relevant to adolescents' digital lives, complemented by live demonstrations and question-and-answer sessions. Evaluation through an interactive quiz revealed an average increase of 58 percentage points across all comprehension indicators, from 33% before to 91% after the socialization, with the highest increase recorded on the 2FA knowledge indicator at 63 percentage points. This activity demonstrates that a context-based socialization approach grounded in adolescents' real digital experiences is more effective in building cyber security awareness than generic technical approaches, while contributing to shaping a generation that is not only digitally proficient but also digitally safe.

Automatically Retrained Machine Learning System for Rice Yield Prediction Using Open-Meteo and BPS Data Ulfatun Nadifa; Ikhsan Hidayat; Wildan
Jurnal Teknik Elektro Vol. 18 No. 1 (2026)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v18i1.41368

This study proposes a machine learning–based rice yield prediction system with a self-updating mechanism, using Gorontalo Province, Indonesia, as a case study. The system integrates daily climate data from Open-Meteo with agricultural statistics from the Central Bureau of Statistics (BPS) to support data-driven decision-making in agriculture. A key challenge addressed in this study is the limited availability of yield data, which are provided only at an annual scale for the period 2018–2024, without seasonal labels. To overcome this limitation, a temporal disaggregation approach is adopted to construct initial seasonal yield labels (M1, M2, M3). These constructed labels serve as approximations, enabling the development of a seasonal prediction model under data-constrained conditions. Several machine learning algorithms, namely Gradient Boosting, Random Forest, XGBoost, Ridge Regression, and Linear Regression, are evaluated using Leave-One-Out Cross-Validation (LOO-CV). Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that ensemble-based models outperform linear baselines, with Gradient Boosting providing the best balance between prediction accuracy and model stability. The main contribution of this study is the design of an adaptive prediction system with a self-updating mechanism that supports periodic retraining and dynamic model evaluation. At its current stage, this mechanism is positioned as an initial framework rather than a fully validated continuous learning system. The proposed system is implemented as a web-based platform that supports yield prediction and planting season recommendations, providing a scalable foundation for intelligent agricultural systems in data-limited environments.

Analisis Kelayakan Sistem Instalasi Listrik Rumah Tinggal di Kabupaten Luwu Rahmad Hidayat Dongka; Ade Irawaty Tolago; Wildan Wildan; Aristi Ayuningsi Ode Asri; Nur Alam Fajar
Dewantara Journal of Technology Vol. 6 No. 2 (2026)
Publisher : Akademi Teknologi Industri Dewantara Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59563/djtech.v6i2.359

Penelitian ini bertujuan untuk menganalisis kelayakan instalasi listrik rumah tinggal di Desa Lempoacci, Kecamatan Suli, Kabupaten Luwu berdasarkan standar Persyaratan Umum Instalasi Listrik (PUIL) 2011. Penelitian ini menggunakan metode deskriptif kuantitatif untuk mengetahui tingkat kesesuaian instalasi listrik rumah tinggal terhadap standar yang berlaku. Teknik pengambilan sampel menggunakan purposive sampling dengan kriteria rumah memiliki instalasi listrik aktif berdaya 450 VA hingga 900 VA dan telah digunakan lebih dari 15 tahun. Dari total 150 rumah, diperoleh 38 rumah yang memenuhi kriteria penelitian. Hasil penelitian menunjukkan bahwa kondisi instalasi listrik rumah tinggal di Desa Lempoacci secara umum masih tergolong baik, dengan tingkat kesesuaian terhadap standar PUIL 2011 sebesar 65,79%, sedangkan ketidaksesuaian sebesar 34,21%. Ketidaksesuaian yang ditemukan umumnya terdapat pada sistem pembumian (grounding) yang belum memenuhi standar. Kondisi instalasi listrik yang tidak sesuai standar berpotensi membahayakan keselamatan penghuni akibat kejut listrik serta meningkatkan risiko kebakaran akibat hubung singkat. Oleh karena itu, diperlukan upaya perbaikan dan peningkatan kesadaran masyarakat terhadap pentingnya penggunaan instalasi listrik yang sesuai standar guna menciptakan keamanan dan keselamatan dalam penggunaan energi listrik di rumah tinggal.

Studi Pembeban Transformator Distribusi Pada PT. PLN (Persero) Rayon Sungguminahasa Wildan Wildan; Rahmat Rahmat; Hendy Prasetyo; Sulfikar Sulfikar
EEICT (Electric, Electronic, Instrumentation, Control, Telecommunication) Vol 9 No 1 (2026)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/eeict.v9i1.23092

Jenis Penelitian ini merupakan jenis penelitian deskriptif kuantitatif yaitu memberikan gambaran dan menganalisis tentang pembebanan transformator distribusi pada PT. PLN (Persero) Rayon Sungguminasa. Pada kondisi ideal, transformator tiga fasa memiliki besaran yang sama pada setiap fasanya; perbedaannya hanya terletak pada sudut fasa yang seharusnya berjarak 120°. Namun, dalam praktiknya kondisi ideal ini sulit tercapai karena masing-masing fasa pada sisi sekunder umumnya menyalurkan daya ke beban yang berbeda-beda. Akibatnya, terjadi ketidakseimbangan beban pada setiap fasa. Hasil analisa data yang diperoleh menunjukkan bahwa pembebanan pada transformator distribusi di wilayah kerja PT. PLN (Persero) Rayon Sungguminasa masih menunjukkan adanya beberapa transformator distribusi yang melebih batas maksimum 80%, dan untuk hasil analisa data ketidakseimbangan beban juga menunjukkan adanya beberapa transformator distribusi yang melebihi batas maksimum 25%.

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