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All Journal ComEngApp : Computer Engineering and Applications Journal ELKHA : Jurnal Teknik Elektro JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) CCIT (Creative Communication and Innovative Technology) Journal Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Kanda Januar Miraswan
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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PCA-Based on Feature Extraction and Compressed Sensing for Dimensionality Reduction Desiani, Anita; Maiyanti, Sri Indra; Miraswan, Kandak Januar; Arhami, muhammad
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.541 KB) | DOI: 10.18495/comengapp.v8i2.281

Abstract

Compressive sensing reduces the number of samples required to achieve acceptable reconstruction for medical diagnostics, therefore this research will implement dimensional reduction algorithms through compressed sensing for electrocardiogram signals (EKG). dimensional reduction is performed based on the fact that ECG signals can be reconstructed with linear combination coefficients with a bumpy base of small measurements with high accuracy. This study will use PCA for feature extraction on ECG signals. The data used are the ECG patient records on the website page www.physionet.org as many as 1200 with each attribute as many as 256 attributes. The total data dimension used is 1200x256, which means the data has 1200 rows and has as many as 256 columns. To show the accuracy of the dimensional reduction result, so it is performed classification on data using KNN and Naive Bayes. The classification results show that KKN can classify well with 84,02% accuracy rate and the Naive Bayes accuracy is 65,78%. for 100 dimensions The conclusion is those dimensional reductions for ECG data that have large dimensions, it still able to provide valid information like it uses the original data. Principle Component Analysis is a good method for reducing data dimensions by selecting certain features, so the dimensions of the data become smaller but still able to provide good accuracy to the reader.
Representasi Pengetahuan Ontologi untuk Klasifikasi Topik Penelitian pada Bidang Ilmu Informatika Rodiah, Desty; Miraswan, Kanda Januar; Kurniati, Junia; Irawan, Dellin; Ardani, Vanya Terra
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.244

Abstract

Research in informatics often involves multiple subdisciplines, making topic classification challenging. Typically, text classification in natural language processing requires training and testing. However, ontology-based classification eliminates the need for training data. Challenges in ontology-based classification include finding terms that lack similarity with ontology and ensuring accuracy in measuring data similarity with knowledge representation. To address this, the fastText method identifies term similarities between words and ontology, while the Wu-Palmer method measures semantic similarity and relationships within ontology. The research process includes preprocessing (Casefolding, Tokenizing, Stopword Removal, Lemmatization), Query Processing (Query Reduction, Duplicate Removal), Word Embedding with fastText, and Semantic Similarity measurement using Wu-Palmer. The dataset consists of 200 research studies from Fasilkom Unsri informatics students' final projects. The classification results show that 178 out of 200 topics were correctly classified, achieving an accuracy of 89.5%, demonstrating the system’s effectiveness.
Perbandingan Prediksi Curah Hujan Kota Palembang Menggunakan Metode Fuzzy Time Series Chen dan Lee Miraswan, Kanda Januar; Utari, Meylani
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.245

Abstract

Prediksi curah hujan dapat dilakukan untuk mengetahui kondisi cuaca dimasa yang akan datang. Curah hujan menjadi salah satu faktor yang sangat penting dalam mengelola sumber daya air, menghindari banjir dan mendukung kegiatan ekonomi dan sosial serta pengambilan keputusan. Kota Palembang, sebagai ibu kota Provinsi Sumatera Selatan, memiliki curah hujan yang relatif tinggi sepanjang tahun. Prediksi curah hujan yang dilakukan secara tradisional menggunakan model statistik dan fisik memiliki beberapa keterbatasan, seperti keterbatasan data dan keterbatasan model statistik yang cocok digunakan. Maka daripada itu, penelitian ini menerapkan metode Fuzzy Times Series (FTS) yang dikenal efektif dalam menangani deret waktu yang tidak pasti dan fluktuatif dalam memprediksi curah hujan di kota Palembang, dimana model yang akan digunakan adalah model Chen dan Lee. Data curah hujan Kota Palembang dari tahun 2015 s.d 2023 yang diperoleh dari Badan Pusat Statistik (BPS) yang akan digunakan pada penelitian ini. Berdasrkan implementasi predeksi dengan Fuzzy Time Series Chen dan Lee diperolehan prediksi yang dapat mendekati data aktualnya, Hasil perhitungan nilai error dari RMSE dan SMAPE untuk model Chen sebesar 122.472 dan 56.41%, sedangkan perhitungan nilai error dari perhitungan RMSE dan SMAPE untuk model Lee sebesar 123.578 dan 57%, Nilai error ini menunjukkan bahwa perolehan prediksi curah hujan kota Palembang memiliki kinerja yang mendekati baik dengan menggunakan metode Fuzzy Time Series Chen dan Lee. Berdasarkan hasil perbandingan kedua model, dapat disimpulkan bahwa model Chen sedikit lebih baik dibandingkan dengan model Lee dalam memprediksi curah hujan di Kota Palembang.
Aplikasi QR-code untuk sistem daftar hadir: Solusi digitalisasi administrasi di SMA dan SMK Rodiah, Desty; Yusliani, Novi; Abdiansah; Utami, Alvi Syahrini; Miraswan, Kanda Januar; Marieska, Mastura Diana; Yunita; Rini, Dian Palupi
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 8 No 2 (2025)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v8i2.22696

Abstract

Kebijakan Merdeka Belajar dari Kemendikbud RI mendorong guru untuk menerapkan pendekatan pengajaran yang fleksibel dan adaptif melalui integrasi teknologi dalam kegiatan pembelajaran. Dalam konteks ini, program pengabdian kepada masyarakat memberikan pelatihan untuk mengembangkan sebuah aplikasi daftar hadir berbasis QR-Code menggunakan Python untuk guru SMA dan SMK. Aplikasi ini dirancang untuk mencatat kehadiran siswa secara cepat, tepat, dan efisien, serta mendukung kemudahan administrasi dan memberikan pengalaman langsung dalam penggunaan teknologi pemrograman. Kegiatan pengabdian ini menerapkan metode Participatory Action Research (PAR), yang meliputi lima tahap: To Know (menggali kebutuhan mitra melalui survei), To Understand (mengevaluasi pelatihan sebelumnya), To Plan (menyusun materi dan instrumen evaluasi), To Act (melaksanakan pelatihan melalui presentasi dan praktikum), dan To Change (melakukan evaluasi). Evaluasi dilakukan dengan pendekatan N-Gain dan skala Likert. Hasil N-Gain sebesar 20,90% menunjukkan efektivitas pelatihan yang kurang meskipun terdapat peningkatan nilai rata-rata sebesar 7,37 poin. Hal ini dipengaruhi oleh latar belakang peserta yang sudah berpengalaman, sehingga materi dan soal perlu dikembangkan lebih lanjut. Di sisi lain, hasil Likert menunjukkan persepsi peserta yang sangat positif. Kendala koneksi internet sempat memengaruhi praktikum, namun narasumber dan mahasiswa aktif membantu peserta yang mengalami hambatan tersebut.
Performance Comparison of Naive Bayes and Support Vector Machine Methods in Music Genre Classification Based on Audio Signal Feature Extraction Using Mel-Frequency Cepstral Coefficients (MFCC) Naserwan, Kevin Putrayudha; Miraswan, Kanda Januar; Utari, Meylani
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11770

Abstract

Music genre classification has gained increasing attention with the emergence of digital music platforms. One of the relevant features extracted from audio signals is Mel-Frequency Cepstral Coefficients (MFCC), which is widely recognized as an effective technique. MFCC features are extracted at the frame level and aggregated at the clip level to represent each music track, making them suitable for audio-based classification tasks. This study applies Naïve Bayes and Support Vector Machine (SVM) algorithms for classification using the GTZAN dataset consisting of 1,000 audio files from 10 music genres, each with a duration of 30 seconds. The performance of these methods is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM demonstrates superior performance, achieving an accuracy of 95.25% compared to 50.37% for Naïve Bayes. This performance gap can be attributed to SVM’s ability to model non-linear decision boundaries and effectively handle high-dimensional MFCC feature spaces. The main contribution of this study lies in the systematic evaluation of multiple SVM kernel configurations and parameter settings, providing empirical insights into the robustness of classical machine learning methods for MFCC-based music genre classification. This study concludes that SVM is better than Naive Bayes in music genre classification with MFCC feature extraction.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Abdiansyah Ahmad Fali Oklilas Ahmad Gustano Akbar, Muhammad Rafi Al Ashri, Muhammad Rizky Al Farissi Alvi Syahrini Utami Anita Desiani Anna Dwi Marjusalinah Ardani, Vanya Terra Arhami, Muhammad Ari Wedhasmara Arief Wijaya Auzan Lazuardi Buchari, Muhammad Ali Darmawahyuni, Annisa Dellin Irawan Desty Rodiah Dhiya Fairuz Dian Palupi Rini Eni Triningsih Firdaus Firdaus Hidayatullah, Altundri Wahyu Irawan, Dellin Junia Kurniati Junia Kurniati, Junia Kartika Rahmayani M. Fachrurrozi . Marjusalinah, Anna Dwi Mastura Diana Marieska Meylani Utari Muhammad Fachrurrozi Muhammad Fachurrozi Muhammad Husein Nashr Muhammad Ukkasyah Naserwan, Kevin Putrayudha Novi Yusliani Nur ‘Azah Osvari Arsalan Redo Jufarda Rendyansyah Sarmayanta Sembiring septi ana Sri Indra Maiyanti Vanya Terra Ardiani Wiwik Anum Puspita Yogi Pratama YUNITA Yunita Yunita