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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi SITEKIN: Jurnal Sains, Teknologi dan Industri Sosial Budaya Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization JURNAL MANAJEMEN MOTIVASI Seminar Nasional Teknologi Informasi Komunikasi dan Industri JOURNAL SPORT AREA J I M P - Jurnal Informatika Merdeka Pasuruan Jurnal Nasional Komputasi dan Teknologi Informasi Building of Informatics, Technology and Science Jurnal GeoEkonomi Jurnal Sistem Komputer dan Informatika (JSON) Journal of Education Informatic Technology and Science Edu Sportivo: Indonesian Journal of Physical Education Jurnal Computer Science and Information Technology (CoSciTech) Journal Of Artificial Intelligence And Software Engineering
Novi Yanti
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Neuroscience Social Sciences Transportation Other

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The use of tennis racket media among SEA Games athletes in Cambodia: How does it affect receiving sepak takraw? Hakim, Abdul Aziz; Saifullah, Ronny; Pelana, Ramdan; Hanafi, Moh.; Yanti, Novi
Journal Sport Area Vol 9 No 1 (2024): April
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/sportarea.2024.vol9(1).14712

Abstract

Background Problems: Receiving the first ball is a problem for national sepak takraw athletes at the 2023 Cambodia Sea Games, where the reception technique is in the bad category. Research Objectives: This study aims to determine the effect of using tennis racket media on SEA Games athletes in Cambodia on improving sepak takraw reception skills. Methods: This study used quantitative methods with a one-group pretest and posttest. Random sampling was used to select Cambodian SEA Games sepak takraw athletes as research subjects. The population in this study were men’s sepak takraw athletes from the 2023 Cambodia Sea Games national training, with the sample used totaling 12 athletes taken randomly. Findings and Results: The results showed that the use of tennis racket media had a significant effect on increasing acceptance of sepak takraw games. Athletes who follow the training programme with tennis racket media experience a consistent increase in their ability to receive the ball.  Conclusion: These results indicate that the use of this additional training tool can be an effective strategy for improving the performance of sepak takraw athletes. The study makes some significant contributions to understanding the use of racket media in the training of sepak takraw athletes. Further research and wider experimentation can help deepen the understanding of the potential use of tennis racket media in sports training.
Implementation of SMOTE and Information Gain Feature Selection in Learning Vector Quantization for Asthma Disease Classification Diah Ayu Kinanti; Fitri Insani; Novi Yanti; Muhammad Affandes
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9354

Abstract

Asma adalah penyakit pernapasan akibat peradangan saluran udara di paru-paru yang menyebabkan penyempitan dan kesulitan bernapas. Prevalensinya terus meningkat secara global, sehingga diperlukan metode deteksi dini yang akurat. Masalah yang ditemukan dalam proses pengklasifikasian penyakit asma adalah distribusi kelas yang tidak seimbang pada dataset. Penelitian ini menerapkan algoritma Learning Vector Quantization (LVQ) yang dioptimalkan dengan seleksi fitur Information Gain dan teknik penyeimbangan data SMOTE untuk klasifikasi penyakit asma. Dataset penelitian mencakup 2.392 data pasien dengan 28 fitur dan 1 kelas target yang diperoleh dari platform Kaggle. Pengujian dilakukan pada lima skenario dengan tiga fungsi jarak Euclidean , Chebyshev, Manhattan, learning rate 0,001–0,005, dan rasio pembagian data 90:10, 80:20, serta 70:30. Hasil terbaik diperoleh pada skenario SMOTE, Information Gain, dan LVQ menggunakan fungsi jarak Euclidean  dengan learning rate 0.004 dan rasio 90:10, menghasilkan akurasi 77.97%, precision  73.61%, recall  87.22% dan F1-score 79.84%. Penerapan SMOTE menjadi komponen penting karena tanpa SMOTE model gagal mengenali kelas asma, terbukti pada percobaan tanpa menggunakan SMOTE menghasilkan precision , recall , dan F1-score bernilai 0% meskipun akurasi mencapai 94–95%.
Perbandingan Kinerja Random forest dan SVM Pada Klasifikasi Tingkat Kekumuhan Permukiman Menggunakan SMOTE Nurika Dwi Wahyuni; Fadhilah Syafria; Novi Yanti; Surya Agustian
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10101

Abstract

Classifying slum levels is essential for a structured, data-driven analysis of settlement conditions. This study compares the performance of Random forest and Support vector machine (SVM) in classifying slum levels in Pekanbaru City across two scenarios with and without SMOTE using slum indicator scoring data. Its contributions include analyzing SMOTE's impact on model performance and evaluating the top 10 features against the full feature set. The dataset comprises 992 RT-level records from Disperkim Pekanbaru City (2020, 2021, and 2023) featuring 16 slum indicator scores based on PUPR Ministerial Regulation No. 14/2018, categorized into three classes: Non-Slum, Low Slum, and Moderate Slum. Following the KDD process (selection, preprocessing, transformation, data mining, evaluation, and analysis), the data was split 80:20 using stratified sampling and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. Results show that the Linear SVM without SMOTE achieved perfect evaluation metrics (1.0000); however, this is interpreted cautiously as the class labels derive from strict regulatory scoring rules, making class boundaries inherently linear. Random forest saw its F1-score rise from 0.9660 to 0.9700 after SMOTE, while the most significant improvement occurred in SVM RBF, jumping from 0.9214 to 0.9779. Testing the top 10 features led to a decreased F1-score across models, indicating that utilizing all 16 features remains optimal for this dataset.
Comparative Study of Agglomerative Hierarchical Clustering and K-Means for Student Academic Stress Grouping Irfan Arifin; Iwan Iskandar; Elvia Budianita; Novi Yanti; Fitri Insani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10265

Abstract

Academic stress is a common problem experienced by college students due to high academic demands, parental expectations, and social pressures during their college years. The high levels of academic stress experienced by students underscore the need for a data-driven approach to more accurately identify and map students’ stress levels. This research aims to compare the performance of the Agglomerative Hierarchical Clustering (AHC) and K-Means methods in clustering students’ academic stress levels and to determine which method produces the best clustering quality. Data were obtained from the distribution of the Perception of Academic Stress Scale (PAS) questionnaire, consisting of 18 statement items, with 361 valid respondents from the Informatics Engineering Program at UIN SUSKA Riau, class of 2022–2025. The selection of the best linkage method in AHC was performed using the Cophentic Correlation Coefficient (CCC), where Ward Linkage was selected with the highest CCC value of 0.8180. Comparative evaluation was conducted using the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index for variations in the number of clusters from K=2 to K=7. The test results showed that AHC Ward Linkage with K=2 was the best configuration with a Silhouette Coefficient of 0.4407 and a Davies-Bouldin Index of 0.8373, outperforming K-Means, which only excelled in the Calinski-Harabasz Index with a value of 419.7405 The clustering resulted in two clusters: High Stress with 244 students (67.6%) and Low Stress with 117 students (32.4%). The 2023 and 2024 cohorts had the highest proportions of high stress at 90.4% and 90.6%, respectively. This research contributes empirical evidence comparing hierarchy-based and partition-based clustering methods for academic stress data, while also demonstrating the use of the Cophenetic Correlation Coefficient as an objective basis for linkage method selection in AHC. It is hoped that the results of this study can serve as a basis for the institution in designing targeted mental health intervention programs for students.
KLASIFIKASI SENTIMEN MASYARAKAT TERHADAP KASUS TUNTUTAN 17+8 MENGGUNAKAN NAÏVE BAYES CLASSIFIER Arif Kurniawan; Muhammad Fikry; Novi Yanti; Surya Agustian
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 16, No 1 (2026): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v16i1.4947

Abstract

Perkembangan media sosial telah mendorong munculnya berbagai opini masyarakat terhadap isu-isu publik, termasuk kasus Tuntutan 17+8 yang menjadi perhatian luas di Indonesia. Analisis sentimen menjadi pendekatan yang penting untuk mengidentifikasi kecenderungan opini masyarakat secara sistematis. Namun, data teks pada media sosial umumnya bersifat tidak terstruktur dan mengandung berbagai noise sehingga memerlukan tahapan preprocessing yang tepat sebelum dilakukan klasifikasi. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap kasus Tuntutan 17+8 menggunakan metode Naïve Bayes Classifier serta mengevaluasi pengaruh tahapan preprocessing terhadap performa model melalui pendekatan ablation study. Data penelitian berupa komentar TikTok yang diproses melalui tahapan preprocessing meliputi case folding, normalisasi, stopword removal, dan stemming. Selanjutnya, fitur teks diekstraksi menggunakan TF-IDF dan diklasifikasikan menggunakan algoritma Multinomial Naïve Bayes. Hasil penelitian menunjukkan bahwa setiap kombinasi preprocessing memberikan pengaruh yang berbeda terhadap performa model. Pada dataset tambahan sebanyak 8.370 komentar, performa terbaik diperoleh pada kombinasi case folding dan normalisasi dengan akurasi 89,87% dan F1-score 91,89%. Sementara itu, pada dataset utama sebanyak 1.525 komentar, performa terbaik diperoleh pada kombinasi normalisasi dan stopword removal dengan akurasi 81,20% dan F1-score 80,31%. Hasil evaluasi menggunakan confusion matrix menunjukkan bahwa model memiliki kemampuan klasifikasi yang baik, terutama pada kelas sentimen positif. Penelitian ini membuktikan bahwa pemilihan tahapan preprocessing yang tepat berperan penting dalam meningkatkan performa klasifikasi sentimen menggunakan Naïve Bayes Classifier.
Team Game Tournament (TGT)-type cooperative learning model: How does it affect the learning outcomes of football shooting? Rubiyatno; Perdana, Rahmat Putra; Supriatna, Eka; Yanti, Novi; Suryadi, Didi
Edu Sportivo: Indonesian Journal of Physical Education Vol. 4 No. 1 (2023): Edu Sportivo: Indonesian Journal of Physical Education
Publisher : UIR Press Bekerjasama dengan International Association of Physical Education and Sports

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/es:ijope.2023.vol4(1).12130

Abstract

Football shooting is a technique of kicking the ball towards the goal with the aim of obtaining winning numbers in a match. However, there are still many students who have difficulty shooting in football games. Therefore, it is necessary to have special treatment in order to get satisfactory shooting results in a game. This study aims to prove the effect of the team game tournament (TGT) type cooperative learning model on the learning outcomes of shooting football. In this study, the type of experiment used was a pretest and posttest for one group. The subjects in this study were students of class VIII-A at SMP Negeri 2 in the 2022–2023 academic year. In this study, saturated sampling technique was used, so 31 students were obtained as samples. Data analysis in this study was assisted by using the SPSS Version 26 application. This study obtained a significance value of 0.000 0.05, and based on these results, the team game tournament (TGT) type cooperative learning model has a significant effect on the learning outcomes of basketball shooting. The conclusion is that the TGT type cooperative learning model treatment has a significant effect on shooting learning outcomes, so these results can be applied to improve learning outcomes in shooting football games. The results of this study provide additional references for sports teachers and sports practitioners related to the TGT type cooperative learning model so that this model can be applied in physical education learning, especially the shooting material of soccer games.
TANTANGAN PROFESI AKUNTAN DI ERA SOCIETY 5.0; ‎INTEGRASI INOVASI ARTIFICIAL INTELLIGENCE (AI) DAN ‎INTERNET OF THINGS (IoT) DALAM AKUNTANSI Setiawati, Erni; Rohmah, Siti; Yanti, Novi
Jurnal GeoEkonomi Vol. 15 No. 1.2024 (2024): EDISI KHUSUS SEMNAS FEB-UNIBA 2024
Publisher : Program Studi Manajemen Fakultas Ekonomi dan Bisnis Universitas Balikpapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/geoekonomi.v15i1.2024.447

Abstract

Penelitian ini bertujuan untuk mengeksplorasi tantangan yang dihadapi profesi akuntan dalam era Society 5.0, khususnya terkait dengan inovasi teknologi digital artificial intelligence (AI) dan internet of things (IoT). Penelitian menggunakan pendekatan kualitatif deksriptif dan induktif, dengan metode analisis, yaitu analisis sekunder dan analisis studi referensi/pustaka. Teknik pengumpulan data dengan cara mengakses dan mengumpulkan data melalui database online dari sumber-sumber yang teridentifikasi; seperti ebook, ejournal (artikel-artikel penelitian), dokumen elektronik, dan sumber informasi lainnya yang relevan dengan judul penelitian. Hasil penelitian menyimpulkan bahwa profesi akuntan menghadapi berbagai tantangan di era society 5.0 yang didorong oleh perkembangan teknologi digital kecerdasan buatan artificial intelligence (AI) dan internet of things (IoT), yaitu: profesi akuntan berpotensi mengalami kerentanan dan harus beradaptasi dengan perkembangan zaman, perlu mengembangkan keterampilan digital agar dapat memahami dan menggunakan teknologi terkini, pemahaman tentang big data, analisis data, dan penggunaan perangkat lunak akuntansi yang terintegrasi. Akuntan harus memahami dan menguji prototipe teknologi baru dan memahami bagaimana teknologi ini dapat diterapkan dalam praktik akuntansi seperti blockchain, machine learning, dan robotika. Akuntan perlu mengikuti pendidikan yang bersertifikasi secara internasional dan relevan, untuk membantu memahami standar akuntansi global dan mengikuti perkembangan terbaru. Akuntan harus responsif terhadap perubahan dalam industri, bisnis, dan teknologi. Lembaga pendidikan perlu mengembangkan kurikulum yang berbasis pada kemampuan manusia-digital untuk pemahaman tentang teknologi dan keterampilan yang relevan di era digital. Dengan mengatasi tantangan-tantangan tersebut, maka para akuntan akan dapat berkontribusi pada keberhasilan society 5.0 dan memastikan keberlanjutan profesi akuntansi di masa depan.
Beyond the Fear of Missing Out: Mapping Research on FOMO in Digital Marketing and Consumer Behavior - A Systematic Literature Review Yanti, Novi; Christine
JURNAL MANAJEMEN MOTIVASI Vol 22 No 2 (2026): Jurnal Manajemen Motivasi
Publisher : Universitas Muhammadiyah Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29406/jmm.v22i2.9276

Abstract

Fear of Missing Out (FoMO) has become increasingly relevant in digital marketing and consumer behavior, yet existing research remains fragmented across psychological, social media, and marketing perspectives. This study systematically reviews FoMO research published in ABDC-ranked journals from 2016–2026. Using a domain-based systematic literature review and PRISMA approach, 27 articles were analyzed. The findings identify three major themes: consumer psychological characteristics, social media and digital environments, and digital marketing persuasion strategies. FoMO emerges as a central psychological mechanism linking these factors to consumer behavior. The study proposes an integrative framework and directions for future research and responsible digital marketing practice.
Co-Authors Albis Ya Albi Arif Kurniawan Arif Pratama Budiman Christine Defi Telly Krisnawaty Palingu, Defi Telly Krisnawaty Diah Ayu Kinanti Diah Yudhawati Didi Suryadi Edi Setiawan Eka Pandu Cynthia Eka Supriatna Elin Haerani Elvia Budianita Fadhilah Syafria Fauzan, Habibul Fazrol Rozi, Fazrol Fitri Insani Fitri Nova, Fitri Hakim, Abdul Aziz Hanafi, Moh. Irfan Arifin Ivo Dwi Ananda Iwan Iskandar kurnia, fitra Lola Oktavia Mardalisa, - Marliana Safitri Miftahul Ghifrah Sembiring Muhammad Affandes Muhammad Fikry Muhammad Irsyad MUHAMMAD YUSUF Nasriah Nasriah Nazruddin Safaat H Nurika Dwi Wahyuni Pizaini Pizaini Primawati, - Rahmad Kurniawan Rahmat Putra Perdana Ramdan Pelana Ramli Renea Shinta Aminda Reski Mai Candra Rianto, Louis Rubiyatno Ruslan Abdul Gani Saifullah, Ronny Sarjon Defit Setiawan, Adil setiawati, erni Siti Rohmah SONY SUHANDONO Surya Agustian Tjahyanto, Teddy Uray Gustian, Uray Yelvi Fitriani Yelvi Fitriani Yusra, Yusra