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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik INFOKAM Mozaik Humaniora JAIS (Journal of Applied Intelligent System) Jurnal DISPROTEK JURTEKSI INTEK: Informatika dan Teknologi Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Vokasi Amalee: Indonesian Journal of Community Research & Engagement BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Ilmiah Teknik Informatika (TEKINFO) Scientific Journal of Informatics Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Akhmad Pandhu Wijaya, Akhmad Pandhu
Universitas Wahid Hasyim
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Transportation Other

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Perbandingan Algoritma Klasifikasi Random Foresst dengan Naïve Bayes Classifier pada Studi Penyakit Berdasarkan Pola Nutrisi Akhmad Pandhu Wijaya
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 1 (2025): Volume 9 Nomor 1 Januari 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i1.14652

Abstract

Penelitian ini bertujuan untuk membandingkan efektivitas algoritma klasifikasi Random Forest dan Naïve Bayes dalam mengklasifikasikan penyakit berdasarkan pola nutrisi. Dataset yang digunakan mencakup atribut seperti usia, jenis kelamin, tinggi badan, berat badan, tingkat aktivitas, preferensi makanan, dan sejumlah informasi terkait nutrisi, termasuk kalori, protein, gula, sodium, karbohidrat, dan serat. Dalam penelitian ini, dua metode klasifikasi yang umum digunakan dalam data mining, yaitu Random Forest dan Naïve Bayes Classifier (NBC), diterapkan untuk menganalisis data dan memprediksi penyakit yang mungkin timbul berdasarkan pola konsumsi makanan. Proses preprocessing data melibatkan pembersihan data, penanganan nilai hilang, dan normalisasi variabel numerik. Untuk evaluasi model, digunakan matriks kinerja seperti akurasi, presisi, recall, dan F1-score untuk menilai kemampuan model dalam memprediksi penyakit secara tepat. Hasil penelitian menunjukkan bahwa model Random Forest memiliki performa terbaik dibandingkan NBC, dengan tingkat akurasi yang lebih tinggi serta presisi dan recall yang lebih baik. Temuan ini mengindikasikan bahwa algoritma Random Forest lebih efektif dalam mengklasifikasikan penyakit berdasarkan pola nutrisi dibandingkan dengan Naïve Bayes Classifier. Kesimpulannya, penelitian ini menunjukkan bahwa algoritma Random Forest lebih unggul dibandingkan Naïve Bayes Classifier dalam mengklasifikasikan penyakit berdasarkan pola nutrisi. Dengan hasil yang menunjukkan tingkat akurasi, presisi, dan recall yang lebih tinggi, Random Forest terbukti lebih efektif dalam menangani kompleksitas data yang melibatkan berbagai atribut nutrisi. The results show that the Random Forest model has the best performance compared to NBC, with a higher level of accuracy and better precision and recall. These findings indicate that the Random Forest algorithm is more effective at classifying diseases based on nutrient patterns compared to the Naïve Bayes Classifier. In conclusion, this study shows that the Random Forest algorithm is superior to the Naïve Bayes Classifier in classifying diseases based on nutritional patterns. With results that show higher levels of accuracy, precision, and recall, Random Forest has proven to be more effective in handling the complexity of data involving various nutritional attributes.
Pengaruh Virtual Meeting dalam Efektivitas Pembelajaran dimasa Pandemi Naufal Maulana Suharijono; Dwipo Setyantoro; Akhmad Pandhu Wijaya
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 6 No 2 (2022)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v6i2.505

Abstract

Pandemi Covid-19 menyebabkan banyak perubahan kondisi di masyarkat termasuk pergesaran dalam proses belajar mengajar. Pemerintah meminta sekolah dan perguruan tinggi di seluruh Indonesia untuk melakukan pembelajaran daring di rumah masing-masing. Pembelajaran daring dinilai sebagai alternatif agar dapat melakukan pembelajaran jarak jauh dan juga untuk memutus rantai persebaran Covid-19 di lingkungan sekolah. Penelitian ini bertujuan untuk menganalisis pengaruh belajar daring atau virtual meeting terhadap efektivitas pembelajaran para pelajar. Penelitian ini dilakukan di Desa Krikilan dan Desa Semambung, Kecamatan Driyorejo, Kabupaten Gresik. Pengambilan data menggunakan metode stratified random sampling dengan instrument kuesioner. Hasil penelitian akan menunjukan apakah virtual meeting terbukti efektif terhadap pebelajaran atau tidak. Hasil penelitian ini juga akan memberikan solusi yang tepat agar pembelajaran daring dapat bekerja secara maksimal.
Robust Human Gait Recognition with Convolutional Neural Network based on Gait Energy Image Fandy Indra Pratama; Akhmad Pandhu Wijaya; Gilar Pandu Annanto; Avira Budianita; Hairudin Farid Sunanda
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.37383

Abstract

Purpose: Human gait recognition is one of the developments in artificial intelligence technology. Gait recognition is a biometric recognition technique that uses no direct interaction with an object, allowing for identification of individuals based on their gait. However, this recognition faces challenges, including varying camera angles (00 - 1800), so this requires a more in-depth introduction. Methods: Therefore, based on the references, this study proposes using the Gait Energy Image (GEI) and Convolutional Neural Network (CNN) features for in-depth extraction and recognition of each image in the Casia B Dataset, which is then compared with the results of previous studies. Result: The results of this study, with the division of the Casia B Dataset 80% as training data and 20% as testing data and 11 camera angles between 00 - 1800 produced an accuracy rate of 99.48%. Novelty: So the accuracy achieved with this deep learning technique exceeds that of previous research using conventional methods and this gait pattern recognition technique can be used to be implemented in a biometric recognition system based on human gait patterns.
IMPLEMENTATION OF SPEECH RECOGNITION FOR SENTIMENT ANALYSIS WITH A VOICE-TO-VOICE PIPELINE USING THE PROTOTYPE METHOD Ummu Khuzaifah; Akhmad Pandhu Wijaya; Arief Hidayat
Jurnal Disprotek Vol. 17 N0. 2 (2026)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v172.10089

Abstract

Human–computer interaction is increasingly evolving toward more natural and efficient voice-based communication. However, most voice assistant systems still separate speech recognition from users’ emotional analysis, resulting in less adaptive interactions. This study aims to design and implement an integrated speech recognition system that combines Speech-to-Text (STT), Support Vector Machine (SVM)-based sentiment analysis, and Text-to-Speech (TTS) within a unified voice-to-voice pipeline. The system was developed using the prototype method to ensure stable and iterative integration among the modules. The STT module utilizes the Google Web Speech API for speech transcription, while sentiment classification employs the SVM algorithm supported by preprocessing stages, including text normalization and spelling correction. The results show that the system operates in real time with a stable response time ranging from 0.6 to 0.9 seconds. Evaluation of the STT module using the Word Error Rate (WER) metric demonstrated optimal performance, achieving a WER of 0 on the test data. In sentiment analysis testing, the prototype method significantly improved system accuracy from 56.00% in the initial prototype to 85.33% in the final prototype through the addition of training data and model refinement. The integration of the three components using the Flask framework resulted in a virtual assistant capable not only of converting speech into text but also of responding adaptively to users’ sentiments through voice. In conclusion, integrating STT, SVM, and TTS into a unified pipeline effectively improves the quality of voice interaction, enabling more communicative and adaptive human–computer interaction.
Co-Authors Agung Nugroho Agyztia Premana Amin, Avika Apriliyani, Dwi Nur Anisa Arief Hidayat Arif Mustofa Avira Budianita Ayu Shabrina Dasuki, Muhammad Hamam Dias Prihatmoko Dwipo Setyantoro Gilar Pandu Annanto Haikal Makin Syaifudin Hairudin Farid Sunanda Hanim, Efitda Heroweti, Junvidya Heru Agus Santoso Indra Sulistianto M. Subchan Mauludin M. Subchan Mauludin Mahfudlah Fajrie Mas’ud, Muhammad Maulana, Muhamad Amir Muhammad Afnan Wicaksana Muhammad Nashrul Haqqi Mustofa, Tegar Widya Naufal Maulana Suharijono Nur Ariesanto Ramdhan Oktaviani Vera Pratama, Arvian Randitya Pratama, Fandy Indra Rahayu, Apriliza Robert Rizki Yono Rossi Prabowo Sarwido Sarwido, Sarwido Setyantoro, Dwipo Siti Maghfiroh Sofiyulloh, Muhammad Wahyu Stifada Nurul Hayati Suharijono, Naufal Maulana Syaifudin, Haikal Makin Tegar Widya Mustofa Ummu Khuzaifah Wardhani, Diky Wirayudha, Halim