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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Pendidikan Sains Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Pendidikan (Teori dan Praktik) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Ilmiah Flash JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer EDUMATIC: Jurnal Pendidikan Informatika TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Community Development Journal: Jurnal Pengabdian Masyarakat Letters in Information Technology Education (LITE) Indonesian Journal of Instructional Media and Model Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bulletin of Community Engagement Indonesian Journal of Innovation Studies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Journal of Artificial Intelligence and Digital Business Jurnal ilmiah teknologi informasi Asia
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Comparative Analysis of Decision Tree and Random Forest Algorithms for Diabetes Prediction Fadhlullah, Aufar Faiq; Widiyaningtyas, Triyanna
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24388

Abstract

Diabetes Mellitus is a long-term medical disorder marked by high blood glucose levels that raise the risk of early mortality and organ failure. It has become an increasing global health problem, so making an accurate and timely diagnosis is urgently necessary. This study aims to diagnose people with diabetes mellitus by utilizing prediction techniques in data mining using experimental research. The prediction stage for diagnosing diabetes consists of four stages: dataset collection, data pre-processing, data processing, and evaluation. Data was obtained from Electronic Health Records (EHRs), namely the public "Diabetes Prediction Dataset". The pre-processing stage involves data filtering, attribute conversion, and class selection. The data processing utilizes random forests and decision tree models for diabetes prediction. The models were evaluated using accuracy, precision, and recall metrics. The results showed that the Random Forest algorithm produced an accuracy value of 93.97%, precision of 99.88%, and recall of 66.56%, with a computational time of 16s. Meanwhile, the decision tree algorithm produces an accuracy value of 93.89%, precision of 98.73%, and recall of 66.88%, with a computation time of less than 1s. Based on these results, it can be concluded that the Decision Tree algorithm is more effective because the difference in accuracy, precision, and recall values produced by the two algorithms does not have significant differences. However, the Decision Tree algorithm has the advantage of using computational time more effectively, which is needed in detecting diabetes because it is related to someone's life. 
Comparison of Time Series Algorithms Using SARIMA and Prophet in Predicting Short-Term Bitcoin Prices Brilliant, Muhammad Zidan; Widiyaningtyas, Triyanna; Caesarendra, Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4773

Abstract

Digital finance, particularly Bitcoin, has become a global phenomenon with high volatility, posing great challenges for traders in predicting short-term prices. This study compares the performance of the SARIMA and Prophet algorithms in predicting short-term Bitcoin prices using daily closing price data from October 1, 2014, to October 1, 2024. The study utilizes two different data timeframes, a 10-year dataset (2014-2024) and the last 5 years (2019-2024) for comparative analysis. The SEMMA methodology is used to analyze and compare the two algorithms, which consist of the stages Sample, Explore, Modify, Model, and Assess. The experimental results show that SARIMA provides more stable and consistent results with an MAPE value of 1.24% and RMSE of 896.15 in Scenario 1 and an MAPE value of 1.27% and RMSE of 920.24 in Scenario 2. In contrast, Prophet shows different performance in each scenario. In Scenario 1, Prophet shows optimal results but not so good with an average MAPE of 1.74% and an RMSE value of 1214.86. On the other hand, Prophet showed good performance in Scenario 2 with a lower average MAPE of 0.71% and a smaller RMSE of 489.94, indicating Prophet's ability to handle newer and more dynamic datasets. Both models show their respective advantages; SARIMA is better for long and stable historical data, while Prophet is more effective for shorter and dynamic data. This research provides practical insights for traders and investors in choosing the right prediction model, with results for further study in predicting crypto asset prices.
EVALUASI ALGORITMA STRING MATCHING UNTUK DETEKSI PLAGIARISME PADA TEKS AKADEMIK PENDEK: STUDI PERBANDINGAN LEVENSHTEIN SEQUENCEMATCHER DAN RABIN-KARP Rizal, Muhammad Fatkhur; Widiyaningtyas, Triyanna
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 3 (2025): EDISI 25
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i3.6180

Abstract

Plagiarisme dalam tugas akademik merupakan masalah serius yang berdampak negatif pada integritas pendidikan tinggi. Penelitian ini bertujuan mengevaluasi kinerja tiga algoritma string matching, yaitu Levenshtein, SequenceMatcher, dan Rabin-Karp, dalam mendeteksi plagiarisme pada teks akademik pendek. Dataset yang digunakan adalah Short Answer Plagiarism Corpus dengan 100 pasang dokumen. Pengujian dilakukan menggunakan Python 3.13.5 dengan threshold 0.8 untuk Levenshtein dan SequenceMatcher, serta 0.7 untuk Rabin-Karp. Hasil menunjukkan bahwa Levenshtein dan SequenceMatcher memiliki presisi sempurna (1.00), namun menghasilkan nilai recall yang rendah (0.23 dan 0.05). sedangkan Rabin-Karp memiliki recall tertinggi (1.00) tetapi menunjukan nilai presisi yang rendah (0.6). Temuan ini menunjukkan bahwa metode string matching efektif untuk mendeteksi plagiarisme literal (plagiarisme dari sumber salinan teks langsung) namun kurang optimal terhadap variasi parafrase (penulisan ulang atau rewording). Penelitian ini merekomendasikan integrasi metode string matching dengan analisis semantik atau pembelajaran mesin untuk deteksi plagiarisme yang lebih komprehensif.
Domination Numbers in Graphs Resulting from Shackle Operations with Linkage of any Graph Saifudin, Ilham; Widiyaningtyas, Triyanna; Rhomdani, Rohmad Wahid; Dasuki, Moh.
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19675

Abstract

The domination number is the number of dominating nodes in a graph that can dominate the surrounding connected nodes with a minimum number of dominating nodes. This domini number is denoted by γ(G). In this research, we will examine the domination number of the distance between two graphs resulting from the shackle operation with any graph as linkage. This differs from previous research, namely the domination of numbers at one and two distances. This study emphasizes how the results of operations on the shackle are connected to the shackle graph as any graph connects the copy. Any graph here means all graphs are connected and generally accepted. The method used in this research is pattern recognition and axiomatic deductive methods. The pattern detection method examines patterns where a graph's number of dominating points can dominate the connected points around it with a minimum number of dominating nodes. Meanwhile, axiomatic deductive is a research method that uses the principles of deductive proof that apply to mathematical logic by using existing axioms or theorems to solve a problem. The Result of graph S_n with t copies and S_m as linkage, then the two-distance domination number in the graph resulting from the shackle operation is γ_2 (Shack(S_n,S_m,t) )=t-1; graph S_n with t copies and C_m as linkage, then the two-distance domination number in the graph resulting from the shackle operation is γ_2 (Shack(S_n,C_m,t) )={■(t,for 3≤m≤6@⌈n/5⌉(t-1),for m≥7)┤; graph C_n with t copies and S_m as linkage, then the two-distance domination number in the graph resulting from the shackle operation isγ_2 (Shack(C_n,S_m,t) )={■(t-1,for n=3@t,for 4≤n≤5@⌈n/5⌉t,for n≥6)┤ This research provides benefits and adds to research results in the field of graph theory specialization of two-distance domination numbers in the result graph of shackle operation with linkage any graph.
PENERAPAN MESIN SPINNER BERBASIS INVERTER UNTUK MENINGKATKAN PRODUKTIVITAS DAN KUALITAS KACANG METE PADA UMKM DININAS Widiyaningtyas, Triyanna; Mustika, Soraya Norma; Mahandi, Yogi Dwi; Akbar, Muhammad Iqbal; Sujito, Sujito; Falah, Moh Zainul
Jurnal Pengabdian Pendidikan Masyarakat (JPPM) Vol 4 No 2 (2023): Jurnal Pengabdian Pendidikan Masyarakat Vol 4 No 2 (2023)
Publisher : LPPM UNIVERSITAS MUHAMMADIYAH MUARA BUNGO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/jppm.v4i2.1370

Abstract

Cashew nuts are the fruit of the cashew plant, the most important of which is the cashew nut itself. Cashews are one of the most traded nut products. One of the MSMEs located in Singosari, Malang Regency is an MSME engaged in processed food products, namely cashew nuts. This MSME is called Dininas. This business was initiated by Mrs. Dini Mei Nastiti and has been established since 2018. Mrs. Dini's business produces around 10 – 50 kg per day with production in the form of cashew nuts. The problem faced by partners is the slicing process which still uses the conventional method, namely draining naturally by placing it in a container covering it with oil-absorbing paper, and then aerating it. Therefore the solution that is relevant in community service is the application of Inverter-based Spinner machines are more optimal, hygienic, and efficient to increase the amount of production. It is hoped that this community service with the application of Inverter-based Spinner machines can help Dinias SMEs in producing cashew nuts and meet demands from consumers. In addition, this machine is also expected to increase the productivity, quantity, hygiene, and quality of cashew nuts as well as increase profits for business managers.
Application-Level Caching Approach Based on Enhanced Aging Factor and Pearson Correlation Coefficient Zulfa, Mulki Indana; Maryani, Sri; Ardiansyah, -; Widiyaningtyas, Triyanna; Ali, Waleed
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2143

Abstract

Relational database management systems (RDBMS) have long served as the fundamental infrastructure for web applications. Relatively slow access speeds characterize an RDBMS because its data is stored on a disk. This RDBMS weakness can be overcome using an in-memory database (IMDB). Each query result can be stored in the IMDB to accelerate future access. However, due to the limited capacity of the server cache in the IMDB, an appropriate data priority assessment mechanism needs to be developed. This paper presents a similar cache framework that considers four data vectors, namely the data size, timestamp, aging factor, and controller access statistics for each web page, which serve as the foundation elements for determining the replacement policy whenever there is a change in the content of the server cache. The proposed similarCache employs the Pearson correlation coefficient to quantify the similarity levels among the cached data in the server cache. The lowest Pearson correlation coefficients cached data are the first to be evicted from the memory. The proposed similarCache was empirically evaluated based on simulations conducted on four IRcache datasets. The simulation outcomes revealed that the data access patterns, and the configuration of the allocated memory cache significantly influenced the hit ratio performance. In particular, the simulations on the SV dataset with the most minor memory space configuration exhibited a 2.33% and 1% superiority over the SIZE and FIFO algorithms, respectively. Future tasks include building a cache that can adapt to data access patterns by determining the standard deviation. The proposed similarCache should raise the Pearson coefficient for often available data to the same level as most accessed data in exceptional cases.
Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies Hairani, Hairani; Widiyaningtyas, Triyanna; Dwi Prasetya, Didik
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2283

Abstract

The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline for solving unbalanced data problems. The working concept of the SMOTE method is to generate new synthetic data patterns by performing linear interpolation between minority class samples based on k-nearest neighbors. However, the SMOTE method has weaknesses, namely the problem of overgeneralization due to excessive sampling of sample noise and increased overlapping between classes in the decision boundary area, which has the potential for noise data. Based on the weaknesses of the Smote method, the purpose of this research is to conduct a systematic literature review on the Smote method modification approach in solving unbalanced data. This systematic literature review method comprises keyword identification, article search process, determination of selection criteria, and selection results based on criteria. The results of this study showed that the SMOTE modification approach was based on filtering, clustering, and distance modification to reduce the resulting noise data. The filtering approach removed the noise data before SMOTE, positively impacting resolving unbalanced data. Meanwhile, the use of a clustering approach in SMOTE can minimize the overlapping artificial minority data that has noise potential. The most used datasets are Pima 60% and Haberman 50%. The most used performance evaluation on unbalanced data is f1-measure 57%, accuracy 55%, recall 43%, and AUC 27%. The implication of the results of this literature review is to provide opportunities for further research in modifying SMOTE in addressing health data imbalances, especially handling noise and overlapping data. The thoroughness of our literature review should instill confidence in the research community.
Sentiment Analysis and Topic Modelling Using IndoBERTweet and BERTopic for Public Health Issues: Analisis Sentimen dan Pemodelan Topik Menggunakan IndoBERTweet dan BERTopic untuk Isu Kesehatan Publik Sihombing, Wesly M; Widiyaningtyas, Triyanna
Indonesian Journal of Innovation Studies Vol. 26 No. 4 (2025): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i4.1833

Abstract

Background: Public health challenges in Indonesia continue to expand across areas such as mental health, chronic diseases, vaccination debates, and environmental issues. Specific background: The rapid use of platform X provides large-scale public discourse that can be analyzed to understand real-time health discussions. Knowledge gap: Limited studies integrate advanced sentiment and topic modeling tailored to informal Indonesian social media language. Aim: This study analyzes public health conversations on platform X using IndoBERTweet for sentiment classification and BERTopic for topic extraction. Results: From 6,740 processed tweets, neutral sentiment dominated public discussions, while topic modeling produced 44 themes, with mental well-being, vaccination debates, chronic disease concerns, and regional disease reports emerging as key issues. IndoBERTweet demonstrated reliable performance (Weighted F1-Score 0.7822), and BERTopic produced coherent and diverse topics. Novelty: This research combines IndoBERTweet and BERTopic to generate a contextual, adaptive, and real-time mapping of public health discourse in Indonesia. Implications: The findings support data-driven health policymaking, enabling authorities to monitor public perceptions, strengthen communication strategies, and design region-specific interventions. Highlights • Public conversations emphasize mental health and lifestyle-related issues. • Topic modeling identifies diverse clusters, including vaccination debates and endemic diseases. • Integrated sentiment–topic analysis enables real-time mapping of health discussions in Indonesia. Keywords Public Health, Social Media Analysis, IndoBERTweet, BERTopic, Sentiment Classification
Ontology-Based Recommender Systems for E-Learning and Multimedia: A Systematic Literature Review Across Domains Riska, Suastika Yulia; Patmanthara, Syaad; Widiyaningtyas, Triyanna
Indonesian Journal of Instructional Media and Model Vol 7 No 2 (2025): Indonesian Journal of Instructional Media and Model
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/ijimm.v7i2.7405

Abstract

The rapid expansion of digital content across various sectors has led to an overwhelming influx of data, highlighting the need for advanced recommendation systems. Traditional methods such as Collaborative Filtering (CF) and Content-Based Filtering (CBF) face limitations like data sparsity and the Cold Start problem, which affect the accuracy of recommendations. This study explores the use of ontologies in enhancing recommendation systems, aiming to overcome these challenges by providing a semantic framework for better item and user representation. A Systematic Literature Review (SLR) methodology was employed to analyze research from 2021 to 2025, focusing on the application of ontologies in e-commerce, healthcare, education, and employment. The findings demonstrate that ontologies improve recommendation relevance, diversity, and explainability, especially in addressing the Cold Start problem. However, challenges in implementation and interpretation remain. This research contributes to the field by emphasizing the potential of integrating ontologies with Knowledge Graphs (KG) and Graph Neural Networks (GNN) to create hybrid models that enhance the accuracy and transparency of recommendations, guiding future advancements in recommendation systems.
Efektivitas Media Pembelajaran Wordwall pada Elemen Dampak Sosial Informatika Kelas VII SMP Nazhiroh Tahta Arsyillah; Triyanna Widiyaningtyas; Satria Putra Pratama
Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Vol. 5 No. 3 (2025): Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI), 2025 (3)
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/jppi.v5i3.1697

Abstract

Dalam kegiatan pembelajaran, hasil belajar merupakan salah satu peran yang utama karena dengan hasil bealajar tersebut guru dapat memahami tingkat pemahaman yang diperoleh peserta didik dalam upaya memenuhi tujuan pembelajaran. Oleh sebab itu, sebagai pendidik harus mampu memberikan pembelajaran yang interaktif dan menyenangkan. Agar hasil belajar yang diperoleh dapat maksimal. Fokus utama dari penelitian yang dilakukan adalah untuk meningkatkan pencapaian belajar peserta didik pada mata pelajaran informatika elemen dampak sosial informatika dengan pemanfaatan media wordwall. Jenis penelitian yang diterapkan pada peneilitan ini adalah Penelitian Tindakan Kelas (PTK). Penelitian berlangsung dalam dua siklus dengan peserta didik kelas VII-D di SMPN 9 Malang sebanyak 31 peserta didik sebagai subjek penelitian. Pengumpulan data dilakukan secara kuantitatif melalui nilai pretest dan postest, serta melalui proses observasi secara langsung kepada peserta didik. Uji N-Gain score diterapkan untuk menganalisis nilai pretest dan postest peserta didik. Penelitian ini menunjukkan bahwa pada siklus I nilai N-Gain peserta didik yaitu 0,57 atau 57% dengan berkategori sedang dan tingkat keefektivannya cukup efektif. Berikutnya pada siklus II hasil uji N-Gain adalah 0,81 atau 81% dengan berkategori tinggi dan tingkat keefektivannya efektif. Dengan itu, dapat disimpulkan bahwa penerapan media pembelajaran wordwall dapat membantu peningkatan hasil belajar informatika elemen dampak sosial informatika pada peserta didik kelas VII-D SMPN 9 Malang.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Adam Ramadhani P Adiba Qonita Ahmad Farobi Ahmad Fuadi Aji P Wibawa Aji Prasetya Wibawa Ali, Waleed Annas Gading Pertiwi Arif Mudi Priyatno Aya Shofia Mufti Bambang Nurdewanto Bintang Romadhon Binti Afifah Brilliant, Muhammad Zidan Budi Wibowotomo Darwis, Herdianti Dasuki, Moh. Didik Dwi Prasetya Ega Gefrie Febriawan Elta Sonalitha Fadhlullah, Aufar Faiq Fadli Hidayat, M. Noer Falah, Moh Zainul Fitriyah Fitriyah Fitriyah Fitriyah Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Hakkun Elmunsyah Haviluddin Haviluddin Hazizah, Chalista Yulia Heru Wahyu Herwanto I Made Wirawan Imansyah, Pranadya Bagus Indriana, Poppy Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Rizky Rizaldi M. Ardhika Mulya Pratama M. Zainal Arifin Martin Indra Wisnu Prabowo Maryani, Sri Moh Zainul Falah Moh. Robieth Alfan Alhamid Mohamad Yusuf Kurniawan Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nafalski, Andrew Nazhiroh Tahta Arsyillah Nurhidayati Pindo Tutuko Poppy Indriana Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Rendy Yani Susanto Rhomdani, Rohmad Wahid Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Satria Putra Pratama Setiadi Cahyono Putro Shandy Krisnawan Sihombing, Wesly M Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Farida Utami Suastika Yulia Riska Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma