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All Journal Jurnal Algoritma JAIS (Journal of Applied Intelligent System) Jurnal Proaksi Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science JUTI "JURNAL TEKNOLOGI INFORMASI" RISTEC : Research in Information Systems and Technology
Fikri Fahru Roji
Universitas Garut
Computer Science & IT Economics, Econometrics & Finance Education

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Journal : RISTEC : Research in Information Systems and Technology

 1 
The Study of Decision Tree Algorithm For Classification of Student Graduation (Case Study : Faculty of Economics, University of Garut) Fikri Fahru Roji; Rahmawati .; Dendi Ramdani
RISTEC : Research in Information Systems and Technology Vol 2, No 1 (2021): Research in Information Systems and Technology
Publisher : Institut Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.388 KB) | DOI: 10.31980/ristec.v2i1.1215

Abstract

The academic performance is one of aspect which has remained the benchmark of the success in learning activities at the university. The indicator of academic performance in the university is the students able to complete their studies on time. Unfortunately, the problem regarding academic performance was associated with the completion time of student studies in Faculty of Economics,University of Garut. In this research explore the model that able to classify the graduation of student through the data mining classification technique by Decision Tree Algorithm. The classification conducted by evaluating the academic performance based on Semester Performance Index (IPS) and Semester Credit Unit (SKS) during two years in the beginning and use the demographics of students as attributes that will be used in the dataset. Based on the examinations that conducted by using k-fold cross validation, there are 8 attributes that influence the graduation of students. The model that represented able to applied for classify the active students in 2016 and 2017 that indicate accuracy value of 79.53% and recall value was 19.23%.
Review Analysis of SatuSehat Application Using Support Vector Machine and Latent Dirichlet Allocation Modeling Fikri Fahru Roji; Nava Gia Ginasta; Yayan Cahyan; Dinar Rahayu; Dendi Ramdani
RISTEC : Research in Information Systems and Technology Vol 4, No 1 (2023): Riset Sistem dan Teknologi Informasi
Publisher : Institut Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/ristec.v4i1.3312

Abstract

SatuSehat is a contact tracing application that replaces the PeduliLindungi application initiated by the Government of Indonesia with the aim of tracking the Covid-19 Virus. The success of the application can be known by analyzing sentiment reviews. In addition to the high number of reviews, there are also other things that need to be highlighted, namely the pattern of reviews that are not in accordance with refined spelling and diverse topics, so that identifying a topic from a collection of reviews is very difficult and takes a lot of time if done manually by humans. This research describes sentiment analysis and topic modeling on SatuSehat app user reviews. By applying Support Vector Machine (SVM) method for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling, this study reveals the views and trends expressed by users. The analyzed review data from Google Play Store includes 171,428 positive reviews and 131,246 negative reviews. The sentiment analysis results indicated the dominance of positive responses. LDA modeling resulted in 8 identified topics, from health concerns to app appreciation. However, negative topics included vaccination challenges, access issues, and app functionality. This research provides insight into users' perceptions of the SatuSehat app, providing a basis for further development and improvement of the app. Keywords: Sentiment Analysis; Topic Modeling; OneHealth App; SVM; LDA
Topic Modeling in Thesis Titles of Students from the Faculty of Economics Universitas Garut Using Latent Dirichlet Allocation Modeling Fikri Fahru Roji; Dinar Rahayu; Riyad Sabilul Muminin; Dendi Ramdani; Dede Hendrik
RISTEC : Research in Information Systems and Technology Vol 4, No 1 (2023): Riset Sistem dan Teknologi Informasi
Publisher : Institut Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/ristec.v4i1.3380

Abstract

In higher education, the completion of a thesis within a 1-year timeframe is a prerequisite for graduation. The selection of a thesis topic is influenced by personal interest, the expertise of the thesis supervisor, and data availability. This research is designed to analyze the thesis topics of Economics Faculty students at Garut University using the Latent Dirichlet Allocation (LDA) Modeling method. Utilizing quantitative and qualitative approaches, this research applies the concept of big data with techniques such as Data Crawling, Data Preprocessing, and Text Mining. The research successfully conducted topic modeling using the LDA method. The analysis showed that topic modeling with the LDA algorithm resulted in seven common thesis topics used in the students' thesis titles. With this, the research contributes to the understanding and efficacy in the determination of students' thesis topics. It is hoped that the results of this research can be utilized to assist in the efficient completion of theses. Keywords: Topic Analysis; Topic Modeling; Thesis Title; Latent Dirichlet Allocation; LDA
Co-Authors Anak Agung Ketut Sudharmawan Anggraeni, Windi Ariesti Dea Nurmastin Novianti Dede Hendrik Dendi Ramdani Dewi Tresnawati Dinar Rahayu Dinar Rahayu Diqy Fakhrun Shiddieq Eri Satria Evi Puspita Indri Tri Julianto Moch. Rizky Cahyadiputra Muslim Alkautsar Nava Gia Ginasta Rahmawati . Ridian Gusdiana Ridwan Setiawan Salman Fariz Surya Garian Bekti Wati Susilawati Wildan Hidayatul Hamdi Wufron Wufron Yayan Cahyan