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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Psikostudia : Jurnal Psikologi JSR : Jaringan Sistem Informasi Robotik TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pengabdian kepada Masyarakat Nusantara Brilliance: Research of Artificial Intelligence Jurnal Teknologi Sistem Informasi Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Akademika Jurnal TAM (Technology Acceptance Model)
Abidin, Zainil
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Sentiment Analysis of Indonesian National Team in 2024 AFF Using Naive Bayes and KNN Adrian, Rahmad; Aryani, Reni; Abidin, Zainil
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7111

Abstract

Social media platforms like Twitter (now X) serve as key channels for public opinion on major events, including sports tournaments such as the AFF Cup, where sentiments reflect nationalism, criticism, and support. Prior studies have highlighted varying accuracies in sentiment classification for Indonesian football contexts, prompting comparisons of algorithms like Naive Bayes and K-Nearest Neighbors (KNN). This research aims to analyze public sentiment directions towards the Indonesian National Team during the 2024 AFF Cup and compare the performance of Naive Bayes and KNN algorithms. Data comprised 1,918 tweets collected from December 8, 2024, to January 8, 2025, reduced to 1,598 unique entries after preprocessing (cleaning, case folding, tokenizing, filtering, stemming). Sentiments were labeled as positive, negative, or neutral by linguistic experts. TF-IDF vectorized features, and SMOTE addressed class imbalance. Models were trained on 90:10 data splits and evaluated using accuracy, precision, recall, and F1-score, with visualizations including frequency diagrams and word clouds. Neutral sentiments dominated at 49.6%, followed by negative (27.3%) and positive (23.2%). Naive Bayes with SMOTE achieved 79.38% accuracy, outperforming KNN (50-53%). Word clouds revealed supportive terms in positives ("garuda", "semangat"), critical in negatives ("kalah", "pecat"), and factual in neutrals ("indonesia", "piala"). Naive Bayes proves more effective for this dataset, offering insights for team management. Future work should explore advanced models like SVM or BERT and expand data sources for broader generalization.
Stress Among Psychologist Candidates Rachmawati, Arini; Abidin, Zainil
Psikostudia : Jurnal Psikologi Vol 13, No 3 (2024): Volume 13, Issue 3, September 2024
Publisher : Program Studi Psikologi, Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/psikostudia.v13i3.15290

Abstract

There were five participants in this study. The results indicate that the majority of participants showed negative emotional reactions, such as increased anxiety leading to a desire to avoid academic situations. Physical reactions also emerged, such as fatigue, sleep disturbances, and headaches. Some participants attempted to cope with stress by engaging in enjoyable activities or exercising, but some still struggled to cope with stressful conditions and felt desperate about academic demands. A psychology graduate must complete a professional psychology education program to be considered a psychologist. The existence of several requirements and responsibilities to be fulfilled in pursuing education as a psychologist candidate, along with obligations outside the academic scope, can increase the risk of stress. This research is a descriptive qualitative study aimed at illustrating the stress experienced among psychologist candidates. The participants in this study were five graduate students from various universities enrolled in a Master of Professional Psychology program. The research instrument used in this study was an interview guide based on the Perceived Stress Scale (PSS) questionnaire and the data analysis method applied was thematic analysis. The results showed that overall, the participants perceived the demands they faced as psychologist candidates to be stressful situations. Stress reactions included the emergence of negative emotions such as increased anxiety, as well as physical reactions such as fatigue, sleep disturbances, and headaches. Some participants attempted to cope with stress by engaging in enjoyable activities or exercising. However, some participants still had difficulty managing their stress, leading to feelings of hopelessness regarding academic demands. The findings of this study are expected to serve as a reference in developing intervention programs to address stress, particularly among students in professional psychology programs.Sarjana psikologi harus menyelesaikan program pendidikan profesi psikologi untuk dapat dikatakan sebagai psikolog. Adanya sejumlah persyaratan dan tanggung jawab yang harus dipenuhi dalam mengikuti pendidikan sebagai calon psikolog, bersama dengan kewajiban di luar lingkup akademis, dapat meningkatkan risiko stres. Penelitian ini merupakan penelitian kualitatif deskriptif dengan tujuan untuk menjelaskan gambaran stres pada calon psikolog. Partisipan dalam penelitian ini berjumlah lima orang mahasiswa Magister Psikologi Profesi dari beberapa universitas. Instrumen penelitian yang digunakan dalam penelitian ini adalah panduan wawancara yang disusun berdasarkan pada kuesioner Perceived Stress Scale (PSS) dan metode analisis data yang diterapkan dalam penelitian ini adalah analisis tematik (thematic analysis). Hasil penelitian menunjukan bahwa secara keseluruhan, para partisipan mempersepsikan tuntutan yang mereka hadapi sebagai calon psikolog merupakan situasi yang memicu terjadinya stres. Muncul reaksi stres berupa adanya emosi negatif seperti kecemasan yang meningkat dan juga terdapat reaksi fisik seperti kelelahan, gangguan tidur, serta sakit kepala. Beberapa partisipan mencoba mengatasi stres dengan melakukan kegiatan yang digemari atau dengan berolahraga. Namun, sebagian partisipan masih mengalami kesulitan dalam mengatasi kondisi stres tersebut hingga memunculkan perasaan putus asa terhadap tuntutan akademis. Temuan penelitian ini diharapkan dapat menjadi referensi dalam pengembangan program intervensi untuk mengatasi stres, khususnya di kalangan mahasiswa program pendidikan profesi psikologi.
Comparison of SVM and KNN Methods for the Integratin of MyIndiHome into MyTelkomsel Application Siagian, Harul Risina; Setiawan, Dedy; Abidin, Zainil
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7234

Abstract

This study aims to analyze user sentiment toward the merger of the MyIndiHome application into the MyTelkomsel platform conducted by PT Telkom Indonesia. In the digital era, the integration of these two customer service applications represents a strategic step to create a unified digital ecosystem. However, this merger has also generated diverse user responses, reflected in various reviews on the Google Play Store. To analyze these opinions, 1,556 user reviews were collected using the web scraping technique. The preprocessing stage included cleaning, tokenizing, filtering, normalization, stemming, and the application of the Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalance. Two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), were applied to classify sentiments into positive, negative, and neutral categories. The experimental results showed that SVM achieved higher accuracy (86.2% before SMOTE and 84.9% after SMOTE) compared to KNN (83.7% before SMOTE and 67.6% after SMOTE). These results indicate that SVM performs more effectively and consistently in handling high-dimensional text data than KNN. Therefore, SVM is considered a more reliable algorithm for sentiment classification in this context. The findings provide valuable insights for PT Telkom Indonesia in understanding user perceptions, improving service quality, and enhancing user experience following the digital integration of MyIndiHome into MyTelkomsel.
Co-Authors Adrian, Rahmad Apresto, Muhammad Haikal Ariansah, Bimo Arsa, Daniel Aryani, Reni aSaputra, Zaqib Benedika Ferdian Hutabarat Dedy Setiawan EDI SAPUTRA Listiani Safitri, Devi Mh. Khathamy Fhadlullah Haq Syahlevy Mohamad Ilham Muhammad Razi A mutia fadhila putri, mutia fadhila Noverina, Yolla Putri Z., Khairin Nisa Rabiula, Andre Rachmawati, Arini Rahman, Reyvi Saputra, Dandi Juan Saputri, Yosika Dian Siagian, Harul Risina susilawati rahman, ledisma Tri Suratno Ulfa Khaira, Ulfa Utomo, Pradita Eko Prasetyo