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All Journal JOIN (Jurnal Online Informatika) JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Bulletin of Computer Science Research Journal of Informatics Management and Information Technology Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Media Journal of General Computer Science (MJGCS) The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Unama
Jasmir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Komentar Resep Masakan Indonesia dengan Machine Learning dan Fitur Ekstraksi Word2Vec Jusia, Pareza Alam; Rahim, Abdul; Yani, Herti; Mulyadi; Jasmir
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12321

Abstract

Perkembangan teknologi digital telah mendorong meningkatnya aktivitas pengguna dalam mengekspresikan opini melalui media sosial, termasuk komentar terhadap resep masakan Indonesia. Analisis sentimen digunakan untuk memahami kecenderungan opini tersebut secara otomatis menggunakan pendekatan machine learning. Penelitian ini bertujuan untuk menganalisis pengaruh penerapan fitur ekstraksi Word2Vec terhadap peningkatan kinerja tiga algoritma klasifikasi teks, yaitu Naïve Bayes, K-Nearest Neighbor (KNN), dan Random Forest, dalam mengklasifikasikan komentar positif dan negatif pada dataset komentar resep masakan Indonesia. Tahapan penelitian meliputi preprocessing data (tokenisasi, stopword removal, dan stemming), pelabelan sentimen, ekstraksi fitur Word2Vec, serta pengujian model menggunakan pembagian data 80:20. Evaluasi dilakukan menggunakan empat metrik, yaitu accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa penerapan fitur Word2Vec mampu meningkatkan performa seluruh model. Akurasi tertinggi diperoleh pada model Random Forest dengan Word2Vec sebesar 84,07%, disusul Naïve Bayes sebesar 84,07%, dan KNN sebesar 79,70%. Nilai F1-score terbaik juga diperoleh oleh Random Forest sebesar 84,45%. Peningkatan performa ini menunjukkan bahwa representasi semantik Word2Vec mampu memperkaya pemahaman model terhadap makna dan konteks kata dalam teks, sehingga lebih efektif dalam mengidentifikasi sentimen pengguna terhadap resep masakan Indonesia.
PENINGKATAN KETERAMPILAN GURU DALAM WORKSHOP PRESENTASI INTERAKTIF DAN EFEKTIF MENGGUNAKAN CANVA DI PONDOK PESANTREN DARUL ARIFIN Sandra, Dodi; Jasmir; Sani Wijaya, Ibnu; Mulyadi; Sadikin, Ali; Winanto, Eko Arip; Ghufron, Muhammad
Jurnal Pengabdian Masyarakat UNAMA Vol 4 No 2 (2025): JPMU Volume 4 Nomor 2 Oktober 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2025.4.2.2557

Abstract

Perkembangan teknologi digital memberikan peluang besar bagi guru untuk meningkatkan efektivitas pembelajaran melalui media presentasi interaktif. Namun, sebagian besar guru di Pondok Pesantren Darul Arifin masih terbatas dalam pemanfaatan aplikasi desain presentasi modern. Kegiatan PKM ini bertujuan untuk meningkatkan keterampilan guru dalam membuat presentasi interaktif dan efektif dengan menggunakan aplikasi Canva. Pelatihan dilakukan melalui workshop interaktif yang mencakup sesi teori, praktik langsung, serta pendampingan. Evaluasi efektivitas kegiatan dilakukan melalui pre-test dan post-test dengan tiga indikator utama: Pengetahuan Dasar, Keterampilan Teknis, dan Evaluasi Dampak. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada ketiga indikator. Nilai rata-rata Pre-test 58,3% meningkat menjadi 87,6% pada Post-test. Secara rinci: Pengetahuan Dasar meningkat 28 poin (57% → 85%), Keterampilan Teknis meningkat 31 poin (59% → 90%), dan Evaluasi Dampak meningkat 29 poin (59% → 88%). Sebanyak 85% guru menyatakan kegiatan sangat membantu, dan 90% mampu menghasilkan presentasi yang lebih menarik dan sistematis. Dengan demikian, workshop ini berkontribusi positif dalam.
TOGAF ADM - BASED ENTERPRISE ARCHITECTURE FOR TANTAN DIGITAL VILLAGE Bujangdek; Setiawan Assegaff; Jasmir
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7318

Abstract

The acceleration of digital transformation in rural governance requires an integrated information system to ensure efficient, transparent, and accountable public services. Yet, many villages including Tantan Village in Muaro Jambi Regency still operate with fragmented applications and redundant data. This study proposes a comprehensive enterprise architecture blueprint for Tantan Village using the TOGAF ADM framework, specifically adapted to the operational and institutional characteristics of rural public administration. Employing a qualitative case study approach, the research develops a four-layered architecture encompassing business, data, application, and technology domains. Theoretically, this study advances the understanding of how enterprise architecture can be localized for small-scale government entities; practically, it provides a replicable model that supports sustainable digital village transformation in Indonesia.
HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS Jasmir; Rosario, Maria; Irawan, Irawan; Siswanto, Agus; Annisa, Tiko Nur
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7488

Abstract

The development of internet technology and social media has driven the increasing use of sentiment analysis to understand public opinion. This study aims to improve the classification performance of sentiment analysis by proposing a hybrid model that combines FastText-BLSTM and BERT. The dataset used consists of 900 Indonesian-language Netflix app user reviews obtained through crawling using Google Play Scraper. The research stages include text preprocessing, feature extraction using FastText and BERT, and classification using BLSTM, which are then combined in a concatenation layer to produce a richer feature representation. Experimental results show that the FastText-BLSTM-BERT hybrid model provides the best performance with an accuracy of 94.22%, a precision of 95.98%, a recall of 95.68%, and an F1-score of 95.83%. This achievement is superior to the single models of FastText-BLSTM and BERT. The main novelty of this research lies in the integration of contextual embeddings from BERT with subword-level semantic and sequential representations from FastText-BLSTM, which has not been extensively explored in prior studies on Indonesian sentiment analysis. This hybridization demonstrates significant improvement in model generalization and robustness for low-resource language texts
Co-Authors Abdul Haris Abdul Haris Abdul Rahim Afrizal Nehemia Toscany agus Nugroho Agus Nugroho, Agus Agus Siswanto Ali Sadikin Ali, Edi Amroni Annisa, Tiko Nur Arvita, Yulia Aryani, Lies Aulia, Dinda Bella Putri Cahyani Bujangdek Chindra Saputra Despita Meisak Devi Dodi Sandra Dodi Sandra Etriyanti, Siska Evan Alber Fachruddin Faisal Amir Fitriani Gunardi Gustirani, Amalinda Herti Yani, Herti Hetty Rohayani Husaein, Ahmad Ibnu Sani Wijaya Ibnu Sani Wijaya ISW Ilham Tri Maulana Irawan Irawan Jeperson Hutahaean Joni Devitra Jumaryadi, Yuwan Junaidi, Feri Khairuldi Khatina Sari Kinanti, Raudatul Kurniabudi M. Riza Pahlevi. B, S M. Taufik Aufa Maichel Hardi MARDIANA Meisarah, Neisya Muhammad Aji Triatama Muhammad Athorik Alfayyed Muhammad Fahmi1, Said Muhammad Ghufron MULYADI Mutawadiah Nada Aprillia Nazrul Azizi Ni Luh Wiwik Sri Rahayu Ginantra Nopi Desmulyani Noviana, Risma Dwi NOVIANTO, YUDI Nurhayati Nurhayati OSCARIO Pardamean Simanjuntak, Daniel Sintong Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Pramudya, Yuga Pressa Persana Surya Saputra Purnama, Benni Putri, Della Utami Ramdani Saputra Rianti Agustini, Silvia Ridho Riza Pahlevi, Riza ROBY SETIAWAN Rohaini, Eni Rosario, Maria Samsul Lutfi Sani Wijaya, Ibnu saputra, sahril Setiawan Assegaf Sharipuddin SIKA, XAVERIUS takhir, Said Wahyu Hardian, Reza WILLY RIYADI Wiratami, Latifa Yudi Yudi Novianto Yulia Arvita