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All Journal Jurnal Ilmiah Informatika Komputer International Journal of Advances in Intelligent Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Journal of System and Computer Engineering Jurnal Pengabdian Masyarakat Indonesia Jurnal Abdi Masyarakat Indonesia Ilmu Komputer untuk Masyarakat Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Bulletin of Social Informatics Theory and Application Jurnal Medika: Medika JBIMA: Jurnal Pengabdian kepada Masyarakat Linier: Literatur Informatika dan Komputer
Ramdaniah Ramdaniah
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Peningkatan Daya Tarik SMK Negeri 6 Bulukumba dengan Implementasi Teknologi Cloud ramdaniah ramdaniah; Bayu Adrian Ashad
Jurnal Pengabdian kepada Masyarakat Vol 1 No 3 (2024)
Publisher : PT. Edutech Inovatif Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64109/wdn32z07

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) yang dilaksanakan di SMK Negeri 6 Bulukumba, bertujuanuntuk meningkatkan daya tarik sekolah melalui implementasi teknologi digital. SMK Negeri 6 Bulukumbamenghadapi tantangan penurunan jumlah peserta didik dan ketiadaan website resmi sebagai media promosi.Untuk mengatasi permasalahan ini, kegiatan PKM difokuskan pada pembangunan website resmi yang informatifdan pelatihan pengelolaan konten digital bagi guru dan staf. Metode yang digunakan adalah Participatory ActionResearch (PAR), yang melibatkan semua pemangku kepentingan dalam setiap tahap kegiatan. Hasil dari kegiatanini menunjukkan peningkatan signifikan dalam kemampuan digital guru dan staf, serta peluncuran website yangberfungsi sebagai platform informasi dan promosi sekolah. Pelatihan yang diberikan kepada 14 peserta berhasilmeningkatkan kepercayaan diri dan keterampilan mereka dalam menggunakan teknologi digital untuk promosi.Dengan adanya website resmi dan peningkatan kemampuan digital, SMK Negeri 6 Bulukumba kini memiliki saranayang lebih baik untuk menyampaikan informasi mengenai program unggulan dan prestasi sekolah. Kegiatan initidak hanya memberikan dampak positif jangka pendek, tetapi juga menciptakan fondasi yang kuat untukpengembangan sekolah di masa depan, serta berkontribusi pada keberlanjutan operasional sekolah dalammemberikan pendidikan berkualitas.
Design of IoT-Based Energy Meter for Efficiency and Disturbance Detection Bayu, Bayu Adrian Ashad; Ramdaniah, Ramdaniah
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2363

Abstract

The increasing need for energy consumption monitoring has driven the development of systems capable of providing accurate electrical information and detecting disturbances at an early stage. This study aims to design an IoT-Based Energy Meter capable of monitoring electrical parameters in real time and detecting load anomalies as a basis for energy efficiency analysis. The system uses a PZEM-004T sensor and an ESP32 microcontroller to measure voltage, current, power, energy, and power factor (cos φ). The data is transmitted to an IoT platform via a wireless connection so it can be monitored remotely. A Long Short-Term Memory (LSTM) model is applied to identify normal power consumption patterns and detect deviations, while a rule-based method is used to detect critical conditions such as overcurrent. Test results show that the device is capable of performing measurements with high accuracy, with error percentages for voltage, current, power, and cos φ parameters ranging between 0%–5% for three types of loads: iron, electric fan, and refrigerator. The LSTM model also successfully detects anomalies such as power spikes, sudden current changes, and disconnected loads with a confidence level of 0.99–1.00. The integration of IoT, artificial intelligence, and basic protection systems results in a reliable and responsive monitoring device. In the future, this system has the potential to be developed for automatic efficiency analysis and intelligent load control.
Enhancing Polyp Segmentation Using Attention U-Net with CLAHE Ramdaniah, Ramdaniah; Ashad, Bayu Adrian
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2370

Abstract

Colorectal cancer remains one of the leading causes of death worldwide, where early detection of polyps through colonoscopy plays a vital role in prevention. This study aims to enhance polyp segmentation performance by integrating Attention U-Net with Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing technique. The proposed method was evaluated using two benchmark datasets, CVC-ClinicDB as the primary dataset and Kvasir-SEG for cross-domain testing. The model was trained using a combination of Binary Cross-Entropy and Dice losses, with a 70–15–15 split for training, validation, and testing. Experimental results show that applying CLAHE improves segmentation accuracy, achieving Dice and IoU scores of 0.84 and 0.76 on CVC-ClinicDB, and 0.62 and 0.50 on Kvasir-SEG, respectively. Statistical analysis using the Wilcoxon signed-rank test confirmed a significant difference between the baseline and enhanced models. These findings demonstrate that the integration of CLAHE with Attention U-Net effectively improves boundary detection and robustness against illumination variations across datasets, contributing to more accurate and reliable computer-aided diagnosis in colorectal cancer screening.
Analisis Sentimen Pengguna X Terhadap Perkembangan Artificial Intelligence (AI) Menggunakan Algoritma Machine Learning Azmi Fauziah Nur; Yulita Salim; Ramdaniah Ramdaniah
LINIER: Literatur Informatika dan Komputer Vol 1, No 4 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v1i4.2534

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap perkembangan Artificial Intelligence (AI) dengan membandingkan kinerja tiga algoritma machine learning: Naive Bayes (NB), Support Vector Machine (SVM), dan K-Nearest Neighbors (KNN). Dataset yang digunakan dalam penelitian ini terdiri dari 5044 tweet yang dikumpulkan dari platform media sosial X yang merepresentasikan opini dan pandangan pengguna tentang perkembangan AI. Proses pengumpulan data melibatkan pemilihan tweet yang relevan dengan menggunakan keyword terkait perkembangan AI, diikuti dengan tahap preprocessing untuk membersihkan dan menghilangkan noise serta kata yang tidak relevan. Evaluasi dilakukan dengan mengukur akurasi, presisi, recall, dan F1-score setiap algoritma dalam mengklasifikasikan sentimen tweet sebagai positif, negatif, atau netral. Hasil penelitian menunjukkan bahwa algoritma SVM secara signifikan lebih efektif dalam menangani analisis sentimen dibandingkan NB dan KNN, dengan akurasi masing-masing algoritma sebagai berikut: SVM=93%, NB=91%, dan KNN=81%. Penelitian ini dapat memberikan wawasan penting mengenai pemilihan algoritma machine learning yang optimal untuk analisis sentimen dan dapat membantu peneliti dan praktisi dalam memilih metode yang tepat untuk evaluasi sentimen di berbagai topik
Analisis Review Pengguna Terhadap Fitur Baru Whatsapp Menggunakan LSTM Rainanda Darya Saputra; Tasrif Hasanuddin; Ramdaniah Ramdaniah
LINIER: Literatur Informatika dan Komputer Vol 2, No 4 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v2i4.3335

Abstract

Sebagai salah satu platform komunikasi paling banyak digunakan, WhatsApp terus menghadirkan berbagai fitur baru untuk meningkatkan kenyamanan penggunanya. Namun, setiap pembaruan fitur seringkali disertai dengan beragam tanggapan dari pengguna, yang banyak dituangkan melalui ulasan di Google Play Store. Penelitian ini bertujuan untuk mengklasifikasikan sentimen dari ulasan-ulasan tersebut menggunakan metode Long Short-Term Memory (LSTM), sebuah arsitektur Recurrent Neural Network yang unggul dalam menangani data teks yang bersifat sekuensial. Data diperoleh melalui teknik web scraping dan diproses melalui tahapan pra-pemrosesan seperti tokenisasi, pembersihan teks, dan embedding kata. Model LSTM dirancang menggunakan kombinasi layer embedding, bidirectional LSTM, dan dense untuk menghasilkan klasifikasi sentimen ke dalam tiga kategori: positif, negatif, dan netral. Berdasarkan hasil pelatihan, model menunjukkan peningkatan akurasi yang signifikan serta penurunan loss yang stabil, mencapai akurasi validasi di atas 85%. Hasil confusion matrix menunjukkan bahwa model mampu membedakan ketiga jenis sentimen dengan akurasi tinggi dan kesalahan klasifikasi yang minim. Uji coba pada input kalimat baru juga menunjukkan kemampuan model dalam mengenali sentimen secara kontekstual dengan baik. Dengan demikian, pendekatan LSTM terbukti efektif dalam memahami dan menganalisis opini pengguna terhadap fitur-fitur baru WhatsApp
A comprehensive comparative analysis of chicken meat classification techniques through machine learning models Anraeni, Siska; Lahuddin, Harlinda; Ramdaniah, Ramdaniah; Melani, Erika Riski; Amalia, Andi Cici; Amaliah, Tazkirah
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2014

Abstract

This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multi-stage approach including data acquisition, preprocessing, feature extraction, and classification. A total of 1,000 chicken images were obtained, consisting of 800 images for training and 200 images for testing, with a proportion of 80:20. Feature extraction was performed using a combination of the HSI (Hue, Saturation, Intensity) color model to capture the color characteristics of chicken and the Local Binary Pattern (LBP) to extract texture information. Classification was performed using the K-Nearest Neighbor (KNN) algorithm with various K values and distance metrics. The experimental results show that the combination of color and texture features provides higher accuracy than using either feature alone. The best model using HSI and LBP feature extraction with K = 1 and K = 3 in the Euclidean distance metric achieved the highest accuracy of 95.4%. With a promising level of accuracy, this method can be applied in automated inspections in the poultry supply chain, improving food safety and helping consumers make better purchasing decisions. However, the main challenge in this study is the variation in lighting during image capture, which causes the fresh and rotten chicken feature values to overlap, thus hindering perfect classification.
Co-Authors Amalia, Andi Cici Amaliah, Tazkirah Azmi Fauziah Nur Bayu Adrian Ashad Bayu, Bayu Adrian Ashad Dolly Indra Farniwati Fattah Lahuddin, Harlinda Lutfi Budi Ilmawan Mansur Melani, Erika Riski Miftahul Janna Mubarak, Syahrul Muhammad Arfah Asis Muhammad Farhan Hermansyah Mustari, Muhid Nia Kurniati Rainanda Darya Saputra Saidah Suyuti Siska Anraeni Sitti Hajrah Mansyur Sriwijanaka Sriwijanaka Sriwijanaka St. Hajrah Mansyur Sukur, Widianti Tasrif Hasanuddin Yulita Salim