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All Journal Jurnal Simetris JAIS (Journal of Applied Intelligent System) Jurnal Dinamika Ekonomi dan Bisnis Jurnal DISPROTEK Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal SITECH : Sistem Informasi dan Teknologi JUKI : Jurnal Komputer dan Informatika Jurnal Kesehatan Primer Walisongo Journal of Information Technology Journal of Dinda : Data Science, Information Technology, and Data Analytics International Journal Software Engineering and Computer Science (IJSECS) Jurnal Minfo Polgan (JMP) Jurnal Talenta Sipil JTH: Journal of Technology and Health Jurnal Kesehatan Primer Jurnal Publikasi Teknik Informatika
Teguh Tamrin
UNIVERSITAS ISLAM NAHDLATUL ULAMA JEPARA
Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Optimalisasi Algoritma Naive Bayes Dengan Teknik Ensemble Dalam Analisis Sentimen Twitter Pantai Kartini Jepara Muhammad Arqom Anwar; Harminto Mulyo; Teguh Tamrin
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14014

Abstract

Penelitian ini memanfaatkan Twitter untuk menganalisis opini publik tentang Pantai Kartini Jepara, dengan fokus pada optimisasi algoritma Naive Bayes dalam analisis sentimen. Penelitian ini mengidentifikasi bahwa akurasi Naive Bayes terbatas dalam menangani data besar dan kompleks. Tujuan utamanya adalah meningkatkan akurasi dan efisiensi analisis sentimen melalui optimisasi parameter dan teknik ensemble. Metode penelitian melibatkan pengumpulan data Twitter dari 2010–2023, preprocessing data, pelatihan model Naive Bayes, SVM, dan ensemble, serta evaluasi performa menggunakan akurasi, presisi, recall, dan F1-score. Model ensemble yang menggabungkan Naive Bayes dan SVM mencapai akurasi tertinggi sebesar 88,81%, meningkat dari 83,91% pada Naive Bayes dasar dan 86,01% pada SVM, menunjukkan perbaikan signifikan dalam analisis sentimen. Kombinasi algoritma Naive Bayes dengan teknik optimasi dan ensemble meningkatkan akurasi analisis sentimen. Penelitian selanjutnya disarankan untuk mengeksplorasi penerapan model ini pada data yang lebih besar atau platform media sosial lain.
AUDIO FEATURE EXTRACTION FOR PREDICTING VIRAL TIKTOK CONTENT USING THE CONVOLUTIONAL NEURAL NETWORK ALGORITHM Edo Kurniawan; Nur Aeni Widiastuti; Teguh Tamrin
JTH: Journal of Technology and Health Vol. 3 No. 4 (2026): April: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v3i4.833

Abstract

The rapid growth of TikTok has increased interest in identifying factors that influence content virality, particularly audio elements that play a central role in trend formation and user engagement. This study aims to develop a model for predicting TikTok content virality based on audio characteristics using a Convolutional Neural Network (CNN). The proposed approach focuses exclusively on audio information to evaluate its independent contribution to virality prediction without incorporating visual, textual, or engagement-based features. The research employed a quantitative experimental design using audio extracted from publicly available TikTok videos categorized into viral and non-viral classes. Audio signals were preprocessed through normalization and duration standardization before being transformed into Mel-spectrogram representations. These spectrogram images were then used as input to a CNN model for automatic feature extraction and classification. Model performance was evaluated using precision, recall, and F1-score metrics. The experimental results demonstrate that the proposed CNN model effectively distinguished viral and non-viral TikTok content. Evaluation on the testing dataset produced a precision of 0.833, recall of 1.000, and F1-score of 0.909. In addition, Mel-spectrogram visualizations revealed distinct frequency-energy patterns between viral and non-viral audio samples, indicating that acoustic characteristics contain meaningful information associated with content virality. In conclusion, audio features can serve as reliable predictors of TikTok content virality, and the CNN-based framework successfully extracted discriminative acoustic patterns from Mel-spectrogram representations. This study contributes to the fields of audio analytics and social media intelligence by providing an audio-centered approach for early-stage virality prediction prior to content publication.
Analisis Sentimen Terhadap Ulasan Game Mobile Legend di Playstore menggunakan Algoritma Logistic Regression Wildan Anwarul Ma’arif; Sarwido Sarwido; Teguh Tamrin
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2319

Abstract

Meningkatnya jumlah ulasan pengguna aplikasi Mobile Legends di Google Play Store, yang mewakili opini dan pengalaman pemain, merupakan pendorong utama penelitian ini. Tujuan penelitian ini adalah untuk menerapkan algoritma Regresi Logistik untuk menguji sentimen pengguna terhadap game tersebut. Langkah-langkah pra-pemrosesan, ekstraksi fitur dengan TF-IDF, dan pemodelan klasifikasi merupakan bagian dari strategi penambangan data yang memanfaatkan alat penambangan teks. Berdasarkan peringkat, 1.000 ulasan dibagi menjadi tiga kategori: netral, negatif, dan positif. Menurut temuan, model Regresi Logistik mencapai akurasi 0,79, berkinerja terbaik pada sentimen negatif dan cukup baik pada sentimen positif. Namun, karena ketidakseimbangan data, model tersebut tidak mampu mendeteksi sentimen netral. Penelitian ini menyimpulkan bahwa Logistic Regression cukup efektif dalam analisis sentimen teks, namun masih memiliki keterbatasan dalam menangani data tidak seimbang dan konteks bahasa yang kompleks.
RANCANG BANGUN APLIKASI AURELBOOK (AUGMENTED REALITY BOOK) Tamrin, Teguh; Riyana Putri, Aprilia; Muzakki, Muhammad Alie
Jurnal Publikasi Teknik Informatika Vol. 1 No. 2 (2022): Mei : Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v1i2.413

Abstract

Seluruh madrasah di Indonesia melakukan aktivitas pembelajaran secara online, tidak terkecuali di MI Mathalibul Huda Mlonggo. Selama ini pembelajaran hanya dilakukan dengan mentransfer teks, gambar, video. Sehingga kurang interaktif dalam proses pembelajaran. Augmented Reality merupakan teknologi penggabungan antara dunia virtual dengan dunia nyata. Ini menjadikan pengalaman baru yang interaktif dan dapat dimanfaatkan pada pembelajaran. AR dapat digunakan untuk memodelkan proses siklus air secara 3D. User hanya butuh menginstal aplikasi pada smartphone dan scanning QR Code yang telah ditentukan. Setelah terdeteksi QR Code yang digunakan akan menampilkan objek 3D dan informasi yang dicantumkan.Hasil dari penelitian AR ini dapat menampilkan siklus daur hidup air secara 3D dan realtime. Kata Kunci: Augmented Reality, Unity, Marker Based, Media Pembelajaran
Stunting Prediction in Toddlers Using the K-Nearest Neighbor (KNN) Method Based on a Web Application at Batealit Community Health Center, Jepara Lisa Falichatul Ibriza; Gentur Wahyu Nyipto Wibowo; Teguh Tamrin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5553

Abstract

Stunting is still a nutritional problem that exists in Indonesia and it needs immediate intervention in Jepara Regency. At the primary healthcare level, Batealit Public Health Center uses manual anthropometric recording for toddlers' growth assessment. This method can be prone to human recording errors and operational delays which hinder prompt clinical decision-making. To improve this condition, this study develops a web-based system for predicting stunting based on the K-Nearest Neighbor (KNN) algorithm. The research method was applied research with system development using the Waterfall model by processing main variables such as age, weight, and height. We tested the algorithm intensively by trying different neighbor values (k) to obtain the maximum value for accuracy, precision, and recall. From experiments, the KNN algorithm is best at k=3 with a 95.23% accuracy rate; this configuration is better compared to larger k values since they increase misclassification rates on normal and stunted categories. By porting this logic into a web interface, detection moves from being a manual task to an automated one occurring in real-time thus application becomes an essential part of decision support enabling health workers to bypass administrative delays and find stunting much faster more accurately within Batealit service area.
Co-Authors Adi Sucipto, Adi Ahmad Rifan Akmal Farizal Ahmat Wakit Akhmad Khanif Zyen Akhmad Khanif Zyen Akhmad Khanif Zyen, Akhmad Khanif Aprilia Riyana Putri Ariyanto Ariyanto Awwaluddin, Ahmad Habib Aziza, Duwi Nur Bijanto Bijanto Cahyanti, Luluk Dina, Maula Hashina Edo Kurniawan Fitriana, Vera Gun Sudiryanto Harsono, Vilanda Haydar, Muhammad Rifqi Fajrul Heru Saputro Kansania, Istianti Leksono, Bagus Bowo Lisa Falichatul Ibriza Lusi Meriyanti Maula Hashina Dina Muhammad Arqom Anwar Mulyo, Harminto Muzakki, Muhammad Alie Nugroho, Muhammad Bayu Nur Aeni Widiastuti Nur Aeni Widiastuti R Hadapiningradja Kusumodestoni Ricardus Anggi Pramunendar Riyana Putri, Aprilia Sarwido Sarwido Sudiryanto, Gun Syamsul Ma’arif Wibowo, Gentur Wahyu Nyipto Wildan Anwarul Ma’arif Yuliana, Alvi Ratna Zaenal Arifin Zaenal Arifin Zainal Abidin Zainal Abidin