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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) YUME : Journal of Management TEKNOLOGIA TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Data Science and Its Applications Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences Mosharafa: Jurnal Pendidikan Matematika Indonesian Journal of Social Science Research Open Science and Technology eProceedings of Engineering Innovative: Journal Of Social Science Research AMMA : Jurnal Pengabdian Masyarakat Jurnal INFOTEL
Adiwijaya
Universitas Telkom
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Discrete Wavelet Transform (DWT) dan Random Forest untuk Deteksi Kanker Berdasarkan Klasifikasi Data Microarray Monica Triyani; Adiwijaya Adiwijaya; Annisa Aditsania
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.484

Abstract

Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO), in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. The accuracy resulting from the classification of Random Forests is not optimal because microarrays have large dimensional data. Therefore, it is necessary to reduce the dimensions of the Discrete Wavelet Transform (DWT) as a feature to reduce dimensions and increase accuracy in microarray data. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 80%, and central nervous system 83.33%.
Fitur Seleksi pada Data Microarray untuk Deteksi Kanker Berdasarkan Klasifikasi Random Forest Tita Nurul Nuklianggraita; Adiwijaya Adiwijaya; Annisa Aditsania
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.485

Abstract

Cancer is a disease that can affect all organs of humans. Based on data from the World Health Organization (WHO) fact sheet in 2018, cancer deaths have reached 9.6 million. One known way to detect cancer that is with Microarray Technique, but the microarray data have large dimensions due to the number of features that are very much compared to the number of samples. Therefore, dimension reduction should be made to produce optimum accuracy. In this paper, we compare Minimum Redundancy Maximum Relevance (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) to reduce the dimension of microarray data. Moreover, by using Random Forest (RF) Classifier, the performance of classification (cancer detection) is compared. Based on the simulation, it can be concluded that LASSO is better than MRMR because it can produce an evaluation of 100% in lung and ovarian cancer, 92% colon cancer, 93% prostate tumor, and 83% central nervous system.
Method comparison of Naïve Bayes, Logistic Regression, and SVM for Analyzing Movie Reviews Aziz, Muhammad Maulidan; Purbalaksono, Mahendra Dwifebri; Adiwijaya, Adiwijaya
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2644

Abstract

A film can be categorized as a successful film based on the reviews given by the critics. The reviews can range from professional critics to public reviews from the general audience. Due to a large number of reviews and opinions on a film, this study aims to create a sentiment analysis model and compare the methods used to analyze datasets from a movie review. Sentiment Analysis is a method for studying and analyzing opinions, then classifying these opinions into several classes. This research will use the Naïve Bayes method, Logistic Regression, and Support Vector Machine (SVM) to analyze film review data. The film review dataset used is a collection of film reviews taken from the Rotten Tomatoes website and will be pre-processed before implementing the Naïve Bayes, Logistic Regression, and SVM methods. The SVM classifier with 80:20 data splitting has the best performance, with a result of 99.4% accuracy score and 93.5% F1 score.
Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection Putri, Dita Julaika; Dwifebri, Mahendra; Adiwijaya, Adiwijaya
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2944

Abstract

Aside from the Holy Qur'an, Hadith is indeed a life guide that every Muslims in this world must follow. The technology for classifying texts and sentences, including categorizing hadiths, is evolving in tandem with the advancement of the times. The model used to perform classification has also been developed and optimized such as the use of the XGBoost algorithm which is more optimized than the previous tree algorithm. This can also make it easier for us as Muslims to study hadiths by categorizing them according to recommendations, prohibitions, and information. This study conducted text classification of Indonesian translations of hadith texts based on recommendations, prohibitions, and information using the XGBoost algorithm, TF-IDF for its feature extraction, and Chi-Square for its feature selection. In this study, experiments were carried out by changing the order of the preprocessing process for the stopword removal and stemming parts, performing the classification process with and without using chi-square as a feature selection, and adding parameter value during the modeling process with XGBoost and the highest final results obtained were 79% for accuracy, 79% for precision, 78% for recall and 78% for F1-score.
The Role of Transformational Leadership in Managing Human Resources for Organizational Innovation Case Study in A State Electricity Company Amir; Adiwijaya; Firly Juanita Surahman; Syam, Mukhlisah
Indonesian Journal of Social Science Research Vol 5 No 1 (2024): Indonesian Journal of Social Science Research (IJSSR)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijssr.05.01.31

Abstract

Transformational leaders provide autonomy and responsibility to employees, allowing them to take initiative and contribute actively to organizational goals. The aim of this research is to understand the role of transformational leadership in influencing human resource management strategies to support innovation within the State Electricity Company. The research method used in this study is Systematic Literature Review (SLR). The results of this study indicate that transformational leadership is not just about managing organizations but also about building a culture that continuously seeks ways to improve and innovate, ensuring that the State Electricity Company remains competitive and relevant in facing the challenges of the future electricity industry.
Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic Fachri Pane, Syafrial; Adiwijaya, Adiwijaya; Dwi Sulistiyo, Mahmud; Akbar Gozali, Alfian
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2415

Abstract

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.
Analyzing risk factors and handling imbalanced data for predicting stroke risk using machine learning Adiwijaya, Adiwijaya; Ramadhan, Nur Ghaniaviyanto
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1678

Abstract

Stroke is a serious medical condition resulting from disturbances in blood flow to the brain, signaling a chronic health issue that requires an immediate response. Principal risk factors increasing the likelihood of stroke include the presence of pre-existing conditions such as Diabetes Mellitus (DM), hypertension, and high cholesterol levels. Effective preventive measures are crucial to minimize stroke risk, and using predictive methods based on data analysis from the clinical examination dataset over the last three years (2019-2021), known as the general checkup (GCU) dataset, presents an innovative approach. This study aims to predict an individual's stroke risk for the following year. In this context, the study also addresses the preprocessing stage of the GCU dataset, which includes solutions for missing values by substituting them with the statistical mean, label encoding, feature correlation analysis using entropy values, and addressing data imbalance with the Adaptive Synthetic (ADASYN) technique. To evaluate their predictive performance, the research involves comparisons among various machine learning models. The outcome of the experiment shows that the Random Forest model is the best model, with 98.7% accuracy and 63.9% F1-Score. This research highlights the importance of preemptive measures against stroke by utilizing predictive techniques on clinical data, with the Random Forest model proving most effective in forecasting stroke probability.
Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset Ramadhan, Nur Ghaniaviyanto; Maharani, Warih; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.798

Abstract

Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
Explainable Ensemble Learning Framework with SMOTE, SHAP and LIME for Predicting 30-Day Readmission in Diabetic Patients Pinem, Joshua; Astuti, Widi; Adiwijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6977

Abstract

Hospital readmission among diabetic patients poses a significant burden on healthcare systems due to its frequency and associated costs. This study presents a machine learning framework for predicting 30-day readmission in diabetic patients using the Diabetes 130-US Hospitals dataset. The framework integrates data preprocessing, SMOTE for class balancing, ensemble learning, and explainable AI (SHAP and LIME) to enhance both accuracy and interpretability. Multiple models were evaluated, and the best performance was achieved by a weighted ensemble with a recall of 89.43% and an F1-score of 0.6612, indicating strong sensitivity. Explainability analysis using SHAP and LIME highlighted key predictors, notably Medication Change Status and Inpatient Admissions, which are clinically relevant. By combining predictive performance with transparent explanations, the proposed framework offers a practical and trustworthy tool for clinical decision support in managing diabetic readmissions.
Study on agronomical characteristics of several introduced cucumber (Cucumis sativus L.) genotypes Sumiahadi, Ade; Adiwijaya, Adiwijaya
Open Science and Technology Vol. 3 No. 2 (2023): Open Science and Technology
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ost.v3i2.104

Abstract

Cucumber is one of the fruit vegetables favored by the Indonesian people. Its development prospect of commercial cultivation and agribusiness management has been very promising, because the marketing opportunities are not only available domestically, but also abroad. Plant introduction is a process of introducing plants from their place of origin into a new region. This study aims to examine the agronomic characteristics of several introduced cucumber genotypes, and was conducted from October to December 2022 at the experimental field of the Faculty of Agriculture, Jakarta Muhammadiyah University. Randomized Complete Group Design (RCGD) was used with five cucumber genotypes: three introduced genotypes (MIT001, MIT002, and MIT003) and two comparator domestic varieties (Ronaldo and Mercury). The results show that the introduced genotypes produced similar vegetative growth characters to those of their comparator varieties. However, several yield components produced are lower than those of their comparator varieties, as seen from several significantly different parameters. MIT001 and MIT003 produced shorter fruit length than that of their comparator variety (Ronaldo), while MIT002 produced lower plant dry weight and fruit weight per plant than those of its comparator variety (Mercury). MIT003 was able to produce the yield components closest to those of its comparator variety. Mentimun merupakan salah satu sayuran buah yang banyak digemari oleh masyarakat Indonesia. Prospek pengembangan budidaya mentimun secara komersial dan pengelolaannya dalam skala agribisnis semakin cerah, karena peluang pemasaran tidak hanya dilakukan di dalam negeri, tetapi juga mancanegara. Introduksi tanaman merupakan suatu proses memperkenalkan tanaman dari tempat asal tumbuhnya ke suatu daerah (negara) baru. Penelitian ini bertujuan mempelajari karakteristik agronomis beberapa genotipe mentimun hasil introduksi. Penelitian ini dilaksanakan dari bulan Oktober sampai Desember 2022 di lahan percobaan Fakultas Pertanian Universitas Muhammadiyah Jakarta. Rancangan penelitian yang digunakan yaitu Rancangan Kelompok Lengkap Teracak (RKLT) dengan lima taraf genotipe mentimun, yaitu tiga genotipe introduksi (MIT001, MIT002, dan MIT003) dan dua varietas nasional sebagai pembanding (Ronaldo dan Merkuri). Hasil penelitian menunjukkan bahwa setiap genotipe introduksi yang diujikan secara umum memiliki karakter pertumbuhan vegetatif yang sama dengan varietas pembandingnya, namun memiliki beberapa karakter komponen produksi yang lebih rendah dari varietas nasional pembandingnya. Genotipe introduksi MIT001 dan MIT003 memiliki panjang buah yang lebih pendek dari varietas nasional pembandingnya (Ronaldo), sedangkan MIT002 menghasilkan bobot kering tanaman dan bobot buah per tanaman yang lebih rendah dari varietas nasional pembandingnya (Merkuri). Genotipe MIT003 adalah genotipe introduksi yang mampu menghasilkan produksi yang paling mendekati varietas pembandingnya.
Co-Authors A Rakha Ahmad Taufiq Abu Bakar, Muhammad Yuslan Ade Iriani Sapitri Ade Romadhony Ade Sumiahadi, Ade Adhitia Wiraguna Adhitia Wiraguna Aditya Arya Mahesa Adnan Imam Hidayat Adwin Rahmanto Afrian Hanafi Al Faraby, Said Al Mira Khonsa Izzaty Alfian Akbar Gozali Alvi Syah Amalya Citra Pradana Amir Andi Ahmad Irfa ANDI FUTRI HAFSAH MUNZIR Andina Kusumaningrum Andri Saputra Andrian Fakhri Andriyan B Suksmono Anggitha Yohana Clara Aniq Atiqi Aniq Atiqi Rohmawati Anisa Salama Annas Wahyu Ramadhan Annisa Adistania Annisa Aditsania Antika Putri Permata Wardani Aras Teguh Prakasa Ardiansyah, Yusfi Astrid Frillya Septiany Astrima Manik Aziz, Muhammad Maulidan Azmi Hafizha Rahman Zainal Arifin Bambang Riyanto T. Bayu Julianto Bayu Munajat Bayu Munajat Bayu Rahmat Setiaji Bernadus Seno Aji Bernadus Seno Aji Bintang Peryoga Bisma Pradana Brama Hendra Mahendra Chiara Janetra Cakravania Clarisa Hasya Yutika D. R. Suryandari Dana Sulistiyo Kusumo Danang Triantoro Danang Triantoro Murdiansyah Daniel Tanta Christopher Sirait Dany Dwi Prayoga Dany Dwi Prayoga Della Alfarydy Akbar Deni Saepudin Denny Alriza Pratama Desi Sitompul Dewangga, Dhiya Ulhaq Dian Chusnul Hidayati Didi Rosiyadi Didit Adytia Dinda Karlia Destiani Dody Qori Utama Dody Qory Utama Dwi Yanita Apriliyana Dwi Yanita Apriliyana Dwifebri, Mahendra Eko Darwiyanto Eliza Jasin Elza Oktaviana Elza Oktaviana Endro Ariyanto Ergon Rizky Perdana Purba F. A. Yulianto Fachri Pane, Syafrial Fahmi Salman Nurfikri Faris Alfa Mauludy Faris Alfa Mauludy Farudi Erwanda Farudi Erwanda Fathur Rohman Fathurrohman Elkusnandi Fhira Nhita Fikri Rozan Imadudin Firda A. Ma’ruf Firdausi Nuzula Zamzami Firly Juanita Surahman Fuad Ash Shiddiq Gde Agung Brahmana Suryanegara Gheartha, I Gusti Bagus Yogiswara Ghozy Ghulamul Afif Gia Septiana Gia Septiana Gia Septiana Gilang Rachman Perdana Gilang Rachman Perdana Gilang Titah Ramadhani Grace Tika Guntoro Guntoro Guntoro Guntoro Guntoro Guntoro Hadyan Arif Hafidudin . Hafizh Fauzan Hafizh Fauzan Hendro Prasetyo Henri Tantyoko Honakan Honakan I Kadek Haddy W. I Made Riartha Prawira I.G.N.P.Vasu Geramona Ilham Kurnia Syuriadi Ilham Yunirakhman Imadudin, Fikri Rozan Imam Prayoga Indriani Indriani Irene Yulietha Irma Irma Irma Palupi Irwinda Famesa Iyon Priyono Jendral Muhamad Yusuf Zia Ul Haq Jenepte Wisudawati Simanullang K, Kasnaeny Kamal Hasan Mahmud Kemas Muslim Lhaksmana Kemas Rahmat Saleh Raharja Kemas Rahmat Saleh Wiharja Kurnia C Widiastuti Kurniawan W. Handito Laila Putri Lalu Gias Irham Lisa Marianah Lisa Marianah Luke Manuel Daely Mahendra Dwifebri P Mahendra Dwifebri Purbolaksono Mahmud Dwi Sulistiyo Melanida Tagari Melanida Tagari Michael Sianturi Milah Sarmilah Moc. Arif Bijaksana Mochamad Agusta Naofal Hakim Mochammad Naufal Rizaldi Mohamad Irwan Afandi Mohamad Mubarok Mohamad Syahrul Mubarok Mohamad Syahrul Mubarok Mohammad Syahrul Mubarok Monica Triyani Muhammad Afianto Muhammad Enzi Muzakki Muhammad Fauzan Muhammad Feridiansyah Muhammad Ghufran Muhammad Irvan Tantowi Muhammad Kenzi Muhammad Mubarok Muhammad Mujaddid Muhammad Naufal Mukhbit Amrullah Muhammad Nurjaman Muhammad Shiddiq Azis Muhammad Shiddiq Azis Muhammad Surya Asriadie Muhammad Syahrul Mubarok Muhammad Yuslan Abu Bakar Nanda Prayuga Nida Mujahidah Azzahra Nida Mujahidah Azzahra Niken Dwi Wahyu Cahyani Novelty Octaviani Faomasi Daeli Novia Russelia Wassi Nuklianggraita, Tita Nurul Nur Ghaniaviyanto Ramadhan Oscar Ramadhan Pinem, Joshua Pratama Dwi Nugraha Preddy Desmon Purbalaksono, Mahendra Dwifebri Putri, Dinda Rahma Putri, Dita Julaika Raihana Salsabila Darma Wijaya Rendi Kustiawan Reynaldi Ananda Pane Riche Julianti Wibowo Riko Bintang Purnomoputra Riska Chairunisa Rizki Syafaat Amardita Rizky Pujianto Rizma Nurviarelda Roberd Saragih et al., Roberd Rosyadi, Ramadhana Said Faraby Satria Mandala Sekar Kinasih Semeidi Husrin Sheila Annisa Shidqi Aqil Naufal Shuni’atul Ma’wa Sigit Bagus Setiawan St.Sukmawati S. Sugeng Hadi Wirasna Suriyanti Suriyanti Syafrial Fachri Pane, Syafrial Fachri Syahrizal Rizkiana Rusamsi Syam, Mukhlisah Syifa Khairunnisa Talitha Kayla Amory Tati LR Mengko Tesha Tasmalaila Hanif Timami Hertza Putrisanni Tita Nurul Nuklianggraita Triyani, Monica Try Moloharto Untari Novia Wisesty Untari Wisesty Untari. N. Wisesty Untary Novia Wisesty Vina Mutiara Purnama Warih Maharani Widi Astuti Widi Astuti Widi Astuti Winda Christina Widyaningtyas Wisnu Adhi Pradana Yana Meinitra Wati Yoga Widi Pamungkas Yuliant Sibaroni Zahra Putri Agusta Zakia Firdha Razak Zulfikar Fauzi