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All Journal Psikologika : Jurnal Pemikiran dan Penelitian Psikologi COUNSENESIA Indonesian Journal of Guidance and Counseling KATALIS: Jurnal Penelitian Kimia dan Pendidikan Kimia Edukasia: Jurnal Pendidikan dan Pembelajaran Penamas Matemar Jurnal ENDLESS : International Journal of Future Studies Jurnal Bimbingan Konseling Jurnal Ilmu Komputer
Sugiyo
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Religion Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Transportation Other

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PENTINGNYA PENYELARASAN SPMI DAN SPME DALAM IMPLEMENTASI SISTEM PENJAMINAN MUTU INTERNAL PERGURUAN TINGGI: The Importance Of Alignment Of Spmi And Spme In The Implementation Of The Internal Quality Assurance System In Higher Education Sugiyo
Jurnal Matemar (Manajemen dan Teknologi Maritim) Vol. 1 No. 2 (2020): Matemar Desember 2020
Publisher : LPPM Akademi Maritim Nasional Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59225/a2bt6m39

Abstract

Sistem Penjaminan Mutu Pendidikan Tinggi (SPM PT) merupakan standar pendidikan tinggi yang terdiri dari Sistem Penjaminan Mutu Internal (SPMI) yang memiliki siklus tentang Penetapan Standar Dikti, Pelaksanaan Standar Dikti, Evaluasi (pelaksanaan) standar Dikti, Pengendalian (pelaksanaan) standar Dikti dan Peningkatan (pelaksanaan) standar Dikti yang disebut dengan PPEPP. Sedangkan Sistem Penjaminan Mutu Eksternal (SPME) yang memiliki siklus Evaluasi Data dan Informasi, Penetapan Status Akreditasi dan Peringkat Terakreditasi, Pemantauan dan Evaluasi Status Akreditasi dan Peringkat Akreditasi yang disebut dengan EPP. SPMI diimplementasikan pada semua bidang kegiatan perguruan tinggi yaitu Bidang Akademik, yang meliputi bidang pendidikan, bidang penelitian, Bidang pengabdian kepada masyarakat; dan Bidang Non-Akademik yang meliputi Sumber Daya Manusia (SDM), Keuangan, dan sarana dan prasarana. Perencanaan, pelaksanaan, evaluasi, pengendalian, dan pengembangan SPMI dan SPME didasarkan pada Standar Dikti memiliki siklus PPEPP dan metode Evaluasi dilakukan melalui Audit Mutu Internal (AMI), dengan demikian menghasilkan luaran / Output bahwa luaran penerapan SPMI oleh PT digunakan oleh BAN PT atau LAM PT untuk penetapan Status dan peringkat terakreditasi PT dan Program Studi
Analisis Eksperimental Kinerja Transformers, VADER, dan Naive Bayes dalam Analisis Sentimen Teks Bahasa Indonesia: Studi Kasus Komentar Terkait Judi Online Sugiyo; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sentiment analysis is a subfield of Natural Language Processing (NLP) that focuses on detecting and classifying opinions expressed in textual data. In the digital social context, the increasing volume of public comments related to online gambling in Indonesia highlights the need to map public perception. This study aims to conduct an experimental analysis of the performance of three popular sentiment analysis approaches: VADER (Valence Aware Dictionary and sEntiment Reasoner), Naive Bayes, and Transformers-based models, specifically on Indonesian-language text. The dataset consists of public comments from social media and digital platforms containing keywords related to online gambling. The research process involves text preprocessing, data labeling, model training (for Naive Bayes and Transformers), and performance testing. Evaluation metrics include accuracy, precision, recall, and F1-score. The experimental results show that the Transformers model (using IndoBERT) achieves the highest performance in terms of accuracy and generalization ability, while VADER performs less optimally due to its limitations in understanding Indonesian linguistic context. Naive Bayes demonstrates moderate and consistent performance but lacks the capability to capture complex contextual meanings. These findings contribute to selecting appropriate sentiment analysis methods for non-English languages and support the development of more accurate public opinion detection systems in the future
Co-Authors A. Dardiri Hasyim AGUNG BUDI SUSANTO Ali Formen Anggai, Sajarwo Awalya Desti Wahyuning Hasyim, Ahmad Dardiri Mar Ah, Nurmala Khoirul Mufrod Teguh Mulyo Nurul Kholifatun Rafif, Aji Sofyan Ramdani, Tober Ratih Permana Sari Rohmanto, Sodik Rosidin Rosidin Sahin, Arif Sri Mulyani Sunar Sunawan Tera Pertiwi Atiqoh Zuhri