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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) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bulletin of Community Engagement Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM)
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

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Analisis Metode Collaborative Filtering menggunakan KNN dan SVD++ untuk Rekomendasi Produk E-commerce Tokopedia Hazizah, Chalista Yulia; Widiyaningtyas, Triyanna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27793

Abstract

The rapid development of internet technology has driven increased adoption of e-commerce, yet companies face challenges in enhancing users' shopping experiences. To assist users in finding products that match their preferences, relevant recommendation analysis is crucial. This research compares the effectiveness of K-Nearest Neighbors (KNN) and Singular Value Decomposition Plus Plus (SVD++) algorithms for e-commerce product recommendations using the Tokopedia Product Reviews dataset from Kaggle, which contains 40,893 reviews. The study includes data collection and preprocessing steps such as removing duplicates, replacing missing values with the average, and normalizing ratings. KNN and SVD++ are then applied to predict ratings using cosine similarity and factor matrices. Evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) shows that SVD++ outperforms KNN, achieving a lower MAE of 0.161176 and RMSE of 0.185252, compared to KNN's MAE of 0.163964 and RMSE of 0.197045. This indicates that SVD++ is more effective in delivering accuracy and capturing data complexity. The findings highlight the potential to enhance recommendation effectiveness in e-commerce, improving user satisfaction by efficiently matching products to preferences.
Si Pelabuhanna: Game Edukasi Pengenalan Buah-Buahan Mengandung Vitamin A menggunakan Metode Forward Chaining Kurniawan, Rizky Rizaldi; Widiyaningtyas, Triyanna
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27837

Abstract

Providing education in a fun way about fruits that contain vitamin A is important because one of the important benefits of vitamin A is for human vision. The purpose of this study to develop the game Si Pelabuhanna and apply the method of forward chaining to determine the eligibility of players to rise to level 2. Using the Game Development Life Cycle (GDLC) development method with stages used initiation, pre-production, production, testing, and release. Our findings are in the form of Si Pelabuhanna games that have a play menu, material, and information and apply the forward chaining method. The Si Pelabuhanna Game can be used in elementary school children's subjects where the material is about fruits containing vitamin A. Application of forward chaining method by specifying variables to be used to create rules. Rules are used to determine if a player is eligible to advance to level 2. Testing using simulation by testing one by one rule after being applied in the game. Based on the test obtained an accuracy value of 100%. This means that the forward chaining method is successfully applied to determine whether a player is eligible to rise to level 2 in a Si Pelabuhanna game.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Educational Data Mining: Multiple Choice Question Classification in Vocational School Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3499

Abstract

Data mining on student learning outcomes in the education sector can overcome this problem. This research aimed to provide a solution for selecting quality multiple choice questions (MCQ) using the results of students’ mid-semester exams in vocational high schools using a Data Mining approach. The research method used was the Cross-Industry Standard Process for Machine Learning (CRISP-ML) model. Steps to assess the accuracy of analyzing the difficulty level of questions based on student profile data and midterm test results. The data used in this research were the findings of basic computer tests on mid-term exams in mathematics disciplines at vocational high schools. This research used several classification algorithms, including SVM, Naive Bayes, Random Forest, Decision Three, Linear Regression, and KNN. The results of evaluating the classification
Anatomy of Sentiment Analysis in Ontological, Epistemological, and Axiological Perspectives Fadli Hidayat, M. Noer; Dwi Prasetya, Didik; Widiyaningtyas, Triyanna; Patmanthara, Syaad
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1228

Abstract

The aim of this article was to examine sentiment analysis methods from the perspective of the philosophy of science with three approaches, ontological, epistemological and axiological. This research used a qualitative research method (descriptive-analysis) with an ontological, epistemological and axiological approach that uses library research and document studies of previous research results. Data collection was carried out through books and reputable scientific journals on Scopus, ScienceDirect, IEEEXplore and Springer Link. The results of this research showed that sentiment analysis from an ontological perspective describes the definition, development and relationship of sentiment with social reality. Meanwhile, from an epistemological perspective, sentiment analysis is viewed from how the source of knowledge is obtained, explaining the production of sentiment analysis knowledge, and several ways of working that can be applied in studies. Axiologically, sentiment analysis can see the function and value resulting from sentiment analysis, as well as discussing the results of interpretation from sentiment analysis studies. These findings showed the development of sentiment analysis in answering various problems to improve the quality of sustainable services in various fields.
Pemanfaatan Genially dalam Pengembangan Media Interaktif untuk Materi Opini dan Fakta Informatika Kelas VIII Kurniawan, Mohamad Yusuf; Widiyaningtyas, Triyanna; Pratama, Satria Putra
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.v5i3.1739

Abstract

Tuntutan perkembangan teknologi yang semakin pesat mendorong kreativitas pendidik dalam memanfaatkan teknologi untuk kegiatan belajr mengajar. Pemanfaatan teknologi untuk proses belajar yang lebih interaktif bisa dilakukan dengan membuat media pembelajaran yang sesuai dengan kebutuhan peserta didik. Materi opini dan fakta elemen DSI mata pelajaran informatika menjadi pengetahuan penting bagi peserta didik untuk mengembangkan pengetahuan dan keterampilan terkait etika dan tanggung jawab sebagai masyarakat digital. Tujuan dari penelitian yang dilakukan yaitu mengembangkan media belajar interaktif menggunakan Genially dengan melihat kelayakan serta efektifitas media pada materi opini dan fakta informatika kelas VIII. Metode penelitian dan pengembangan (R&D) digunakan dalam penelitian ini, dengan mengadopsi model pendekatan ADDIE melalui 5 tahapan proses pengembangan. Metode pengumpulan data yang dilakukan berasal dari angket uji ahli dan angket pengguna, selain itu terdapat data efektivitas media yang berasal dari perbandingan skor pre-test dan post-test. Perolehan data uji kelayakan media mendapatkan klasifikasi sangat layak dengan skor 93% dan untuk hasil uji kelayakan materi memperoleh skor 83% dengan klasifikasi sangat layak. Media pembelajaran yang dihasilkan terbukti sangat praktis digunakan pengguna dengan rata-rata skor 84%. Pengujian efektivitas media menunjukkan adanya peningkatan rata-rata hasil belajar peserta didik sebesar 11%. Media yang dikembangkan dinilai sangat layak dan terbukti memberikan hasil positif untuk memaksimalkan hasil belajar.
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.
Co-Authors - Ardiansyah Abdul Hadi, Afif Adam Ramadhani P Adiba Qonita Ahmad Farobi Ahmad Fuadi Aji P Wibawa Aji Prasetya Wibawa 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 Fitriyah Fitriyah Fitriyah Fitriyah Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Haviluddin Haviluddin Hazizah, Chalista Yulia Heru Wahyu Herwanto I Made Wirawan Imansyah, Pranadya Bagus Indriana, Poppy Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Mohamad Yusuf Kurniawan, Rizky Rizaldi M. Ardhika Mulya Pratama M. Zainal Arifin Martin Indra Wisnu Prabowo Moh Zainul Falah Mohamad Rodhi Faiz Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa Mulya Pratama, M. Ardhika Nafalski, Andrew Nurhidayati Pindo Tutuko Poppy Indriana Pratama, Satria Putra Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Rajib Muhammad Basthomy Rendy Yani Susanto Rhomdani, Rohmad Wahid Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Setiadi Cahyono Putro Shandy Krisnawan Soenar Soekopitojo Soraya Norma Mustika Sri Maryani Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Waleed Ali Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma