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All Journal Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Annual Research Seminar CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) IJIE (Indonesian Journal of Informatics Education) Jurnal Manajemen STIE Muhammadiyah Palopo MBR (Management and Business Review) JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JURTEKSI JOISIE (Journal Of Information Systems And Informatics Engineering) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Darma Agung Jurnal Bisnis, Manajemen, dan Ekonomi Jurnal Generic Jurnal Pendidikan dan Teknologi Indonesia Djtechno: Jurnal Teknologi Informasi Jurnal Algoritma Jurnal Teknologi dan Manajemen Industri Terapan Indonesian Journal Computer Science (ijcs) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Indonesia Sosial Teknologi The Indonesian Journal of Computer Science Management Analysis Journal Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Buffer Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ken Ditha Tania
Jurusan Sistem Informasi, Universitas Sriwijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Analisis Asosiasi Antara Produktivitas Pelajar dan Manajemen Waktu Berdasarkan Algoritma FP-Growth Rabbani, Muhammad Randy; Theonady, Oktavio; Faizah, Haniyah; Satria, Eka Bayu; Meiriza, Alsella; Tania, Ken Ditha
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12327

Abstract

Penelitian ini bertujuan menganalisis hubungan antara manajemen waktu dan produktivitas pelajar menggunakan algoritma FP-Growth. Data yang digunakan berasal dari dataset Ultimate Student Productivity yang terdiri dari 5.000 data dan 21 atribut. Analisis dilakukan melalui tahapan Knowledge Discovery in Databases (KDD) yang meliputi seleksi data, pra-pemrosesan, transformasi, serta pembentukan association rule berdasarkan nilai support, confidence, dan lift ratio. Hasil penelitian menunjukkan bahwa kategori sedang (medium) mendominasi sebagian besar variabel yang dianalisis. Aturan asosiasi memiliki nilai confidence tinggi dan lift ratio lebih dari satu, yang menunjukkan hubungan signifikan antar variabel. Produktivitas kategori sedang berkaitan dengan durasi belajar dan tingkat fokus yang seimbang, sedangkan kategori rendah berkorelasi dengan hasil akademik yang rendah. Temuan ini menunjukkan bahwa keseimbangan dalam pengelolaan waktu belajar berperan penting dalam membentuk pola produktivitas pelajar. Selain itu, pendekatan berbasis data mampu memberikan gambaran objektif mengenai perilaku belajar siswa. Temuan ini dapat dimanfaatkan untuk mengoptimalkan manajemen waktu belajar guna meningkatkan produktivitas dan capaian akademik pelajar, serta sebagai acuan bagi institusi pendidikan dalam menyusun strategi pembelajaran berbasis data.
Klasifikasi Adopsi Berbasis Kecerdasan Buatan pada UMKM di Indonesia Menggunakan Algoritma Random Forest Muhammad Ihsan Dirgantara; Fakhri Sepriansyah; Nulry Izzatul Maula; Farhan Daffazka; Ken Ditha Tania; Alsella Meiriza
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp35-44

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in the Indonesian economy; however, digital transformation based on artificial intelligence (AI) remains a significant challenge. This study aims to classify AI adoption among MSMEs in Indonesia using the Random Forest algorithm and to identify the factors that influence it. The dataset was obtained from the Zenodo repository, consisting of questionnaire results regarding AI adoption in MSMEs. The research stages included data cleaning, encoding, splitting the data into training (80%) and testing (20%) sets, implementing the Random Forest algorithm, evaluation, and result analysis. The evaluation results show an accuracy of 80.3% with an ROC-AUC of 0.884. The weighted precision, recall, and F1-score values are 81.2%, 80.3%, and 80.4%, respectively. These evaluation results indicate that the Random Forest algorithm performs well on this dataset. Furthermore, the feature importance analysis revealed several influential variables in AI adoption among MSMEs, including strategic decision-making (10.9%), digital leadership (8.3%), and respondent position (7.8%). In conclusion, the implementation of the Random Forest algorithm demonstrates strong performance in classifying AI adoption among MSMEs in Indonesia and highlights key influential variables such as strategic decision-making, digital leadership, and respondent position.
Perancangan Knowledge Management System Berbasis Website Menggunakan Model SECI untuk Mendukung Knowledge Sharing Guru pada SMP Bina Karya Muhammad Ihsan Dirgantara; Fakhri Sepriansyah; Nurly Izzatul Maula; Farhan Daffazka; Ken Ditha Tania; Zaqqi Yamani
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp112-126

Abstract

The design of a website-based Knowledge management system (KMS) using the SECI model at SMP Bina Karya is motivated by several problems, including knowledge that remains stored individually with each teacher, the unavailability of centralized learning materials and lesson plans (RPP), and the difficulty faced by substitute teachers in delivering lessons when replacing the main teacher who is absent. The Knowledge management system serves as a solution to document, distribute, and prevent the loss of knowledge, while also acting as a medium to enhance the culture of knowledge sharing among teachers. The design method used is a qualitative approach consisting of data collection through observation, interviews, and literature studies, identification of knowledge management using the SECI model, system requirements analysis, system design, and testing using Focus Group Discussion (FGD). This study produces a website-based KMS equipped with features such as user account management, substitute teacher schedule management, learning material management, lesson plan management, and discussion forums. The results of the FGD testing show an average acceptance rate of 94.2% for all developed features, with the substitute teacher schedule management feature serving as the main differentiator that successfully addresses the specific problems at SMP Bina Karya.
Implementasi Knowledge Management Berbasis Model SECI di Perpustakaan Daerah Provinsi Sumsel Ummu Farida Muthmainnah; Putri Salsabilah; Zaskia Aulia Wulandari; Talitha Zafirah; Ken Ditha Tania; Zaqqi Yamani A
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp138-142

Abstract

This study analyzes the implementation of knowledge management based on the SECI model at the Regional Library of South Sumatra Province. The method used is qualitative with a case study design through a systematic literature review and digital content analysis of the website, OPAC INLISLITE, social media, and the DiarySumsel application. The results show that the implementation of the SECI model has been carried out across its four stages. Socialization is realized through direct services and mobile libraries using 4 mobile units. Externalization is evidenced by the documentation of circulation service Standard Operating Procedures (SOPs). The combination is implemented through the integration of INLISLITE and DiarySumsel, which served 23,946 users. Internalization is reflected in the adoption of digital systems by librarians and users. Supporting factors include technology availability, management commitment, and extensive service coverage. The challenges faced are limited trained human resources, suboptimal technology utilization, and low community information literacy.
Penerapan Association Rule Menggunakan Algoritma Apriori untuk Rekomendasi Strategi Penjualan pada UMKM Toko Pempek Putri Salsabilah; Ummu Farida Muthmainnah; Zaskia Aulia Wulandari; Talitha Zafirah; Ken Ditha Tania; Alsella Meiriza
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp192-197

Abstract

Pempek Ceria SME has a growing number of daily sales transactions; however, these data have not been optimally utilized to support sales strategies. This situation highlights the need for transaction data analysis to understand customer purchasing patterns and develop more effective promotional strategies. Therefore, this study focuses on applying association rules using the Apriori algorithm to provide sales strategy recommendations for Pempek Ceria SME. The analysis was conducted using RapidMiner software on 317 transactions from October to December 2025, with a minimum support of 15% and a minimum confidence of 65%. The results show two association rules that meet these criteria: the combination of Pempek Adaan and Orange Juice, with a support of 28% and confidence of 72%, and the combination of Pempek Kapal Selam and Sweet Iced Tea, with a support of 27% and confidence of 70%. These findings indicate that the association rule method based on the Apriori algorithm can identify relationships between menu items frequently purchased together. By understanding these purchasing patterns, Pempek Ceria SME can optimize bundling strategies and product recommendations to improve promotional effectiveness and sales.
Knowledge Discovery Based on Sentiment Analysis of Public Perceptions About Generative AI on X Maulizidan, Muammar Ramadhani; Tania, Ken Ditha
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.107758

Abstract

Public discourse surrounding Generative Artificial Intelligence (GenAI) reflects diverse attitudes ranging from optimism to ethical concern, particularly as these technologies become increasingly discussed in educational contexts. This study examines public perceptions of GenAI on the social media platform X using a knowledge discovery approach that integrates multiple topic modeling techniques and Aspect-Based Sentiment Analysis (ABSA). A total of 111,675 English-language tweets collected between June 23, 2024, and June 23, 2025, were analyzed using five topic modeling methods BERTopic, Top2Vec, LDA, LSA, and NMF to identify dominant discussion themes and evaluate topic coherence. Sentiment toward specific GenAI aspects was subsequently examined using ABSA to capture fine-grained public attitudes. The results indicate that topics related to ethics and creativity are predominantly associated with negative sentiment, while innovation and cloud-related discussions show higher levels of positive sentiment. Education-related topics are largely characterized by neutral sentiment, suggesting exploratory and informational discourse. These findings highlight the importance of addressing ethical awareness, trust, and AI literacy in informatics education. By combining multi-model topic analysis with aspect-level sentiment interpretation, this study provides methodological insights and empirical evidence to support responsible GenAI integration in educational contexts.
Knowledge Discovery untuk Identifikasi Atribut Dominan Depresi Mahasiswa Menggunakan Random Forest: Knowledge Discovery for Identifying Dominant Attributes of Student Depression Using Random Forest Nur Salwa Fadia Akmar; Gerri Asa Saputra; M. Suandi; Muhammad Yusuf; Alsella Meiriza; Ken Ditha Tania; Ahmad Rifai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2684

Abstract

Depresi merupakan gangguan kesehatan mental yang umum dialami Mahasiswa dan dipengaruhi oleh faktor sosial, akademik, serta psikologis. Penelitian ini bertujuan untuk mengidentifikasi atribut dominan depresi pada Mahasiswa menggunakan pendekatan data mining dalam kerangka Knowledge Discovery in Databases (KDD). Kebaruan penelitian ini terletak pada integrasi seleksi fitur Weight by Information Gain dan klasifikasi Random Forest untuk mengukur kontribusi atribut secara kuantitatif. Dataset Student Mental Health yang digunakan berjumlah 101 data, dengan distribusi kelas yang tidak seimbang. Hasil menunjukkan bahwa Marital Status merupakan atribut yang paling dominan, diikuti oleh Treatment, Anxiety, dan Panic Attack. Model menghasilkan akurasi sebesar 80,36%. Penelitian ini menunjukkan bahwa pendekatan yang digunakan tidak hanya mampu melakukan klasifikasi, tetapi juga mampu mengidentifikasi faktor dominan depresi pada Mahasiswa secara objektif.
Comparative Evaluation of Machine Learning Algorithms for Diabetes Prediction with SMOTE and Principal Component Analysis Badia Inaya Sazrade; Ken Ditha Tania; Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9903

Abstract

Diabetes mellitus is a chronic disease that requires early detection to reduce the risk of severe complications. However, machine learning-based diabetes prediction is often affected by class imbalance and high-dimensional data. This study investigates the effectiveness of integrating Synthetic Minority Over-sampling Technique (SMOTE) and Principal Component Analysis (PCA) for diabetes prediction. A total of 80,437 records from a Kaggle diabetes dataset were processed using the Knowledge Discovery in Databases (KDD) framework. Six machine learning algorithms, namely Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, and Neural Network, were evaluated using train-test split ratios of 70:30, 80:20, and 90:10. Performance was measured using accuracy, precision, recall, and F1-score. Without oversampling, XGBoost consistently achieved the highest accuracy across all split ratios, peaking at 94.04% at the 80:20 ratio; however, recall for the minority (diabetic) class remained substantially lower than for the majority class, indicating that high overall accuracy masked weaker detection of actual diabetes cases. After applying SMOTE, overall accuracy declined across all models (e.g., XGBoost fell to 87.52% at 80:20), but minority-class recall improved markedly, indicating a more balanced classification between classes at the cost of overall accuracy. Notably, at the 80:20 split, the Neural Network achieved a marginally higher accuracy (87.67%) than XGBoost under SMOTE, although XGBoost remained the top performer at the 70:30 and 90:10 ratios, suggesting that its advantage under class-balanced conditions is not uniform across split ratios. PCA was applied to reduce data dimensionality and did not substantially affect predictive performance; however, the present results do not include quantitative evidence, such as the change in feature count or computation time, needed to substantiate claims about its contribution to efficiency. These findings suggest that XGBoost with an 80:20 split is the most effective configuration when class imbalance is not addressed, while the application of SMOTE narrows the performance gap between models and shifts the trade-off toward more balanced, rather than purely accuracy-maximizing, classification.
Co-Authors Abdillah Putra, Muhafsyah Adeliani, Adeliani Adriansyah, Rizki Afdhal Nadzif, Muhammad Ahmad Rifai Ahmad Rifai Ahmad Rifai Akbar Adiprama, Faris Akbar Kurniawan, Iqbal Akbar, Rifko Akhda, M. Dandi Al Fachrozi, Muhammad Al-Farisy, M Hadi Albani, Muhammad Syarief Albukhori, M Rafli Alfarizi Ramadhiyansa, Muhammad Alfarizi, M. Ali Bardadi Ali Ibrahim Ali Ibrahim (SCOPUS ID: 57203129436) Allsela Meiriza, Allsela Alsella Meiriza Alsella Meiriza Alvines, Mahendi Alzena Aisha Shakira Amanda Ardhani, Dhita Amelia Amelia Amelia Putri, Shinta Amelia, Rita Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Andini, Meisya Dwi Anggun Ramadina Anindya Putri, Salsa Anisa Basulina, Nur Anissa, Cahya Rahmi Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi, Apriyadi Aqil Zidane, Muhammad Aqilah Syahputra, M Fathan Archi Daffa Danendra, Muhammad Ardhillah, Onky Ari Wedhasmara Ariyani, Ishlah Putri Ariyanti, Putri Arvhi Randita Setia Ary Pratama, Muhammad Mayda Athallah Ubaid, Deni Attika Putri, Shopi Audia Faradhisa Ansori Aulia, Cantika Aurelia, Haaniyah Ayuningtiyas, Pratiwi Azmi Zaky, Muhammad Azra, Muhammad Azyumardi Badia Inaya Sazrade Bahri, Cheisya Andini Baidhawi, Alif Bimmo Fathin Tammam Cahya Aulia, Syifa Cahya Rahmi Anissa Catra, Rafa Nadira Cici Elna Sari Citra, Belia Clark Peter Wijaya, Adley Constancio, Elven Dedy Kurniawan Dian Febriansyah Dwiansyah, Octa Dzaky Agusman, Muhammad Eka Saputra Eka Sevtiyuni, Putri Elna Sari, Cici Endang Lestari Ruskan Epriyanti, Nadia Fachrozi, Muhammad Al Fahmi Aulia Hakim, Adzka faizah, haniyah Fajaria, Mutiara Fakhri Sepriansyah Fakhri Sepriansyah Farhan Daffazka Fathoni - Fatihaturrahmah, Aisyah Fatimah, Aisyah Fauzan, Muhammad Fairuz Ferdiansyah Fikri, M Fauzan Firmansyah, Zikri Gerri Asa Saputra Gustiani, Sindy Haidar Afif Mufid, Muhammad Hanggara, Bryan Hendrawan, Deni Agus Hermanto, Muhammad Lucky Hikmahwarani, Fellycia Homausyah, Weli Ratri Ichsan Farel Rachmad, Muhammad Ikhwan Najatafani, Bintang Inayah, Anna Fadilla Indira Nailah Ramadhani Ispahan, Tarisha Izzan Fieldi, Muhammad Jackson Imanuel Manurung Jodi Pratama, Muhammad Jonathan Pakpahan Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Khoiriyah Harahap, Dayana Kurnia Sari, Winda Lakeisyah, Eka Therina Lifiano Jamot Munthe, Gabriel Lubis, Muhammad Ali M Ihsan Jambak M Luthfi Khailani, Kgs M Naufal Hisyam M. Ilham Fahlevi M. Suandi Mahdiyah Afifah Sari Mahdiyah Afifah Sari Maretta, Aulia Pinkan Mariska, Inneke Via Marshella, Siti Hariza Mas Ud, Khalid Al Maulana, Rahmat Maulizidan, Muammar Ramadhani Meiriza, Allsella Meiriza, Alsella Miftahul Falah Mira Afrina Mohd Rizky Putra Pratama Mufidah, Luthfiah Muhammad Adisatya Dwipansy Muhammad Dzaky Alifayoezra Muhammad Idris Muhammad Ihsan Dirgantara Muhammad Luthfi Al-Ghifari Muhammad Luthfi Al-Ghifari Muhammad Yusuf Munaspin, Zahra Diva Putri mutia fadhila putri, mutia fadhila Nabilatulrahmah, Raihana Nachwa, Syakillah Nadrota Acta, Muhammad Fakhri Najibah Putri, Aulia Najwa Widasari, Yesya Naretha Kawadha Pasemah Gumay Nashiroh Ramadhani, Muthia Naufaldihanif, Rihan Novrizal Eka Saputra Nugraha, Allan Nulry Izzatul Maula Nur Salwa Fadia Akmar Nuraini Kusuma, Aisha Nurly Izzatul Maula Onkky Alexander Pacu Putra Prasetia, Dika Pratama Putra, Daffa Pratiwi, Metti Detricia Purba, Kevin Agustin Putri Ariyanti Putri Casanova, Musdalifa Putri Mutiara Arinie Putri Salsabilah Putri Silpiara Putri, Amelia Rizki Putri, Aulia Najibah Putri, Naila Raihana Putri, Salsa Anindya Rabbani, Muhammad Randy Raditya Dafa Rizki Rafika Octaria Ningsih Rafli Maulana, Muhammad Rahmah, Atika Nur Rahman, M. Fadhil Rahmat Izwan Heroza Ramadhan Putra Pratama, Muhammad Ramadhani, Indira Nailah Rangga Aderiyana, Fakih Ravi Wijayanto, Muhammad Riansyah, Muhammad Bintang Naufal Risyahputri, Aliyananda Rizka Mumtaz, Fadia Rizki Ade Ningsih Rizky Herdiansyah, Muhammad Rizkyllah, Anabel Fiorenza Robani, M Tsabita Rositiani, Ely Sabar Manahan, Nico Sabila, Amalia Sahira, Mutia Salsabila, Adella Salsabila, Shofi Sanjaya, Riska Amelia Saputra, Marco Sasmita, Ruth Mei Satria, Eka Bayu Sembiring Depari, Alrayssa Davinka Septhia Charenda Putri Sevtiyuni, Putri Eka Shelly Putri Siade, Shalya Yunia Siregar, Richi Nauli Juniarto Siswahyudianto Suci Amalia Suci Fitriani, Suci Sukamto, Ika Sumiyarsi Sukatin, Sukatin Syarief Albani, Muhammad Talitha Zafirah Theonady, Oktavio Theresia Pardede, Eva Theressa Hasioani Sianturi, Claudia Tika Octri Dieni Titiana, Nuke Merisca Tri Zafira, Zahra Triana, Ayu Triputra, Muhamad Meiko Tsabitah, Laila Ummu Farida Muthmainnah Wahyuni Cahnia Sari Wilantara, M Pandu Winda Kurnia Sari Wirnanti, Rintan Wulan Dari, Atikah Yamani, Zaqqi Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Zahran Afif, Muhammad Zaqqi Yamani Zaqqi Yamani Zaqqi Yamani A Zaskia Aulia Wulandari Zidan, Umar Rahman