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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL TEKNOLOGI DAN OPEN SOURCE Building of Informatics, Technology and Science Jurnal Tekinkom (Teknik Informasi dan Komputer) JISA (Jurnal Informatika dan Sains) Journal of Telenursing (JOTING) Innovation in Research of Informatics (INNOVATICS) Journal of Applied Data Sciences Jurnal Sistem Informasi dan Teknik Informasi DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
Junadhi, Junadhi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Other

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Opinion Mining on TikTok Using Bidirectional Long Short-Term Memory for Enhanced Sentiment Analysis and Trend Prediction Muharnisa Haspin, Wafiq; Junadhi, Junadhi; Susanti, Susanti; Yenni, Helda
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8019

Abstract

The widespread use of TikTok has generated a vast number of user reviews, offering a rich dataset for sentiment analysis. This study aims to classify TikTok reviews from the Google Play Store into positive, negative, and neutral categories, a complex task due to the informal and unstructured text. The research seeks to develop a reliable sentiment analysis model using deep learning to understand user perceptions, aiding platform improvements and marketing strategies. We collected 10,000 reviews via web scraping, preprocessed through text cleaning, normalization, tokenization, filtering, and stemming. Sentiment labels were assigned automatically using a lexicon-based approach, showing predominantly positive reviews. Word2Vec transformed text into numerical vectors for feature extraction. The Bidirectional Long Short-Term Memory (Bi-LSTM) model, with Embedding, Bidirectional LSTM, Dropout, and Dense layers, achieved 80% accuracy and an F1-score of 0.78 using a 90:10 train-test split. While effective for positive and negative sentiments, neutral expressions were less accurately detected due to lower recall. Compared to traditional methods like Naive Bayes, Support Vector Machine, and K-Nearest Neighbors, Bi-LSTM offered superior accuracy and better handling of linguistic variability, making it valuable for analyzing social media feedback.
Evaluation of User Experience Information Systems Using Heuristic Evaluation (Case Study of STMIK Amik Riau Student Portal) Surya, Heru Satria; Millenio, Benino Giordiola; Junadhi, Junadhi; Putri, Silvyana Dwi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i2.1790

Abstract

The academic information system is an important system to supports lecture activities. Is used by almost all elements in the university, both students, lecturers, staff and leaders. This research uses Heuristic Evaluation as an inference method to assess the components of learnability, efficiency, memorability, errors, and satisfaction. for various cases, such as designing academic and corporate websites with reference to these problems, it is necessary to evaluate the usability of STMIK Amik Riau information system. STMIK Amik Riau implements an integrated information system to support fast and real-time information management processes of STMIK Amik Riau information system includes various services such as E-KRS, E-KTM, E-EDOM, and other information. The aim is to identify problems related to the usability of the website. The data collection method in this research was carried out using questionnaire, containing a list of questions distributed via google form to respondents, its about 100 students of STMIK Amik Riau. Based on the analysis conducted using the Heuristic Evaluation method, the evaluation results of STMIK Amik Riau web portal have met the usability criteria with an average of 78.71915% where P > 60% and provides user satisfaction in accessing STMIK Amik Riau web portal. STMIK Amik Riau's web interface design is quite good. These results are based on the results of Likert scale score which states that the respondents agree.
WhatsApp Chat Fraud Analysis Using Support Vector Machine Method Rahman, Fathur; Irfansyah, Irfansyah; Andhika, Rivaldi Dwi; Junadhi, Junadhi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i2.1791

Abstract

Fraud is one of the most cyber crime on social media. One of the popular social media in Indonesia is Whatsapp. Cases of fraud through chat on Whatsapp application often occur in Indonesia, its due to lack of information. The research conducted related to the detection of words containing fraud in WhatsApp chat application. The methods in this research applies the literature study method to find secondary data in the references theories and relevant research. The data collection is carried out by collecting chats that lead to fraud cases and then processing them using RapidMiner application with SVM (Support Vector Mechine) method. The results of this research can be concluded that this research succeeded in implementing SVM algorithm for whatsapp fraud chat analysis with an accuracy rate of 84.21%
Implementation of K-Means Clustering Algorithm for Grouping Traffic Violation Levels in Siak Fauzan, Bias Arbi; Jamaris, M; Junadhi, Junadhi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 5 No. 1 (2022): Jurnal Teknologi dan Open Source, June 2022
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v5i1.2427

Abstract

Traffic offences often occur in different regions, ranging from mild to moderate to severe. The categories of offences include not carrying a Driver's Licence, stnk (Vehicle Number Certificate) or stck (Vehicle Trial Certificate) is invalid, not wearing a seat belt, not turning on headlights during the day and under certain conditions, disobeying traffic signs, disobeying traffic signals. Moderate offences include not having a Driver's Licence, not concentrating while driving and breaking the door of the drawbar. Serious violations include deviating from other vehicles on the road, damaging and interfering with road functions, not insuring one's own responsibility and not insuring staff and passengers. In this study, the K-Means algorithm was used with the aim of obtaining information on data groups of traffic violations based on the time of the incident so that the cause of the traffic violations that occurred in Tasikmalaya City is known. Based on the validation with Davies Bouldin Index metric, 4 clusters were identified which can group the data well. The PerformanceVector results from the assessment of the clusters resulted in 4 clusters with a value of 0.134. Cluster 1 with the most data violations amounting to 74 violations occurred at night, Cluster 2 with the most violations amounting to 16 violations occurred during the day, Cluster 3 with the most violations amounting to 6 violations occurred in the afternoon and Cluster 4 with the most violations amounting to 113 violations occurred in the morning.
Development of Knowledge Management System to Improve the Performance of the New Student Admission System for Higher Education Anam, M. Khairul; Fitri, Triyani Arita; Zoromi, Fransiskus; Junadhi, Junadhi; Nu'man, Nu'man
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1443

Abstract

The New Student Admission System (PMB) is the main door or core business of the University and requires a good management system. Every Academic Year STMIK Amik Riau forms a committee to carry out this PMB activity. The PBM committee consists of several parts, namely the promotion section, the registration section and the selection section.  Each section carries out knowledge sharing or knowledge transfer in carrying out its duties. This knowledge sharing is only limited to informal or formal communication through meetings so that the knowledge sharing process has not been carried out optimally. The purpose of this study was (1) to measure the readiness of human resources in the application of knowledge sharing in terms of the dimensions of knowledge, culture, technology and dimensions and (2) to develop knowledge sharing features in the PMB system to support decision making quickly to increase the business value of the institution. The stages used in this KMS were The 10-Step Knowledge Management Roadmap while the evaluation of the application of KMS used the SECI model. The results obtained in this study are a system that helps new PMB officers learn the STMIK Amik Riau PMB system. so that the new PMB officer does not ask the old officer again.
Analisis Sentimen Kesehatan Mental Pemuda di Media Sosial Menggunakan Deep Learning Agustin, Agustin; Junadhi, Junadhi; Zoromi, Fransiskus; Kudadiri, Parlindungan
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.8112

Abstract

Kesehatan mental merupakan isu yang semakin penting di kalangan pemuda Indonesia, terutama dengan meningkatnya ekspresi emosi negatif seperti stres, kelelahan, dan kecemasan yang sering diungkapkan melalui media sosial. Penelitian ini bertujuan untuk menganalisis sentimen kesehatan mental pemuda menggunakan pendekatan deep learning berbasis Long Short-Term Memory (LSTM) terhadap unggahan publik berbahasa Indonesia di platform X (Twitter). Data dikumpulkan melalui proses web scraping dengan kata kunci yang relevan dan kemudian melalui tahapan pra-pemrosesan, pelabelan manual, serta pembagian data menjadi 80% untuk pelatihan dan 20% untuk pengujian. Model LSTM dibangun dengan arsitektur yang terdiri atas embedding layer, LSTM layer, dropout layer, dense layer, dan output layer beraktivasi Softmax untuk tiga kelas sentimen (positif, negatif, dan netral). Hasil penelitian menunjukkan distribusi sentimen menunjukkan bahwa emosi negatif mendominasi dengan proporsi 45,8%, diikuti oleh sentimen positif sebesar 35,8%, dan netral sebesar 18,4%.Model mampu mencapai akurasi sebesar 87,4% dengan nilai precision dan recall rata-rata sebesar 0,85, yang menandakan kemampuan tinggi dalam mengenali konteks bahasa informal pemuda di media sosial. Analisis distribusi sentimen menunjukkan dominasi emosi negatif yang berkaitan dengan tekanan akademik dan sosial, sementara sentimen positif menggambarkan semangat dan mekanisme adaptasi diri. Temuan ini membuktikan bahwa LSTM efektif untuk deteksi ekspresi emosional berbasis teks serta berpotensi diterapkan sebagai sistem pemantauan digital bagi kesejahteraan mental generasi muda.
Co-Authors Adyanata Lubis Agustin Agustin Anam, M. Khairul Andesa, Khusaeri Andhika, Rivaldi Dwi Asrofiq, Ahmad Desti Puswati Devita, Yeni Edwar Ali Efrizoni, Lusiana Erlinda, Susi Fadliellah, Mardiyat Fathur Rahman Fauzan, Bias Arbi Finanta Okmayura Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Handoko Handoko Helda Yenni, Helda Herwin Herwin Herwin, Herwin Husen, Ratna Andini Irfansyah Irfansyah Jamaris, M Kudadiri, Parlindungan Martina Sinta Kristanti Meidina, Mita Millenio, Benino Giordiola Muharnisa Haspin, Wafiq Muslim Muslim Negara, Ari Sukma Nita, Yureya Nu'man, Nu'man Perumal, Thinagaran Putra, Jean Riko Kurniawan Putri, Silvyana Dwi Rahmiati Rahmiati Retno Sari Sarjon Defit Surya, Heru Satria Susanti Susanti SUSI ERLINDA Triyani Arita Fitri Unang Rio Yoyon Efendi Yuda Irawan