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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JURNAL SISTEM INFORMASI BISNIS Epsilon: Jurnal Matematika Murni dan Terapan Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Mimbar Sekolah Dasar POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Sains dan Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengembangan Riset dan Observasi Teknik Informatika Journal of Computer Science and Informatics Engineering (J-Cosine) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Formil (Forum Ilmiah) Kesmas Respati Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Altasia : Jurnal Pariwisata Indonesia Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Data Science and Software Engineering Lex Journal : Kajian Hukum dan Keadilan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Dwi Kartini, Dwi
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Aerospace Engineering 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 Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation

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Effectiveness of SMOTE in Enhancing Adult Autism Spectrum Disorder Diagnosis Predictive Performance With Missforest Imputation And Random Forest Musyaffa, Muhammad Hafizh; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Kartini, Dwi; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.66

Abstract

Autism Spectrum Disorder (ASD), originally described by Leo Kanner in 1943, is a complex developmental condition that manifests through social, emotional, and behavioral challenges, often including speech delays and difficulties in interpersonal interactions. Despite significant advancements in diagnostic criteria over the years, accurate diagnosis of ASD in adults remains challenging due to limited access to comprehensive datasets and inherent methodological constraints. The Autism Screening Adult dataset used in this study exemplifies these issues, as it contains missing values and exhibits a marked class imbalance, both of which can adversely affect model performance. To address these challenges, we proposed a framework that integrates Random Forest classification with MissForest imputation and the Synthetic Minority Over-sampling Technique (SMOTE). MissForest effectively imputes missing data by employing an iterative random forest approach that preserves the underlying structure of the data without relying on strict parametric assumptions. Meanwhile, SMOTE generates synthetic samples for the minority class, thereby balancing the dataset and reducing prediction bias. Experimental evaluation through 10-Fold Cross Validation demonstrated that the application of SMOTE significantly enhanced model performance. Notably, the overall accuracy improved from 70.17% to 79.32%, and the AUC-ROC increased from 47.13% to 85.84%, indicating a robust improvement in the model’s ability to distinguish between positive and negative cases. These results underscore the critical importance of addressing data imbalance and missing values in predictive modeling for ASD. The promising outcomes of this study provide a solid foundation for developing more reliable diagnostic tools for adult ASD, and future research may further refine feature selection and incorporate additional data sources to optimize performance even further.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9189

Abstract

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
Enhancing Classification of Self-Reported Monkeypox Symptoms on Social Media Using Term Frequency-Inverse Document Frequency Features and Graph Attention Networks Rizian, Rizailo Akfa; Budiman, Irwan; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Ahmad, Umar Ali
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5482

Abstract

Early detection of infectious diseases plays a crucial role in minimizing their spread and enabling timely intervention. In the digital era, social media has emerged as a valuable source of real-time health information, where individuals often share self-reported symptoms that can serve as early warning signals for disease outbreaks. However, textual data from social media is typically unstructured, noisy, and contextually diverse, posing challenges for conventional text classification methods. This study proposes a hybrid model combining Term Frequency–Inverse Document Frequency (TF-IDF) feature representation with a Graph Attention Network (GAT) to enhance the early detection of Monkeypox-related self-reported symptoms on Indonesian social media. A dataset of 3,200 tweets was collected through Tweet-Harvest and subsequently preprocessed and manually labeled, producing a balanced distribution between positive (51%) and negative (49%) samples. TF-IDF vectors were used to construct a document similarity graph via the k-Nearest Neighbors (k-NN) method with cosine similarity, enabling GAT to leverage both textual and relational information across posts. The model’s performance was evaluated using accuracy, precision, recall, and macro-F1, with macro-F1 serving as the primary indicator. The proposed TF-IDF + GAT model achieved 93.07% accuracy and a macro-F1 score of 93.06%, outperforming baseline classifiers such as CNN (92.16% macro-F1), SVM (85.73%), Logistic Regression (84.89%). These findings demonstrate the effectiveness of integrating classical text representations with graph-based neural architectures for improving social media based disease surveillance and supporting early epidemic response strategies.
Gender Classification of Twitter Users Using Convolutional Neural Network Fitra Ahya Mubarok; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi; Triando Hamonangan Saragih
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3318

Abstract

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
Iplementasi Fuzzy Pada Monitoring dan Kontrol Kualitas Air Tangki Pembibitan ikan Menggunakan LabView Andi Farmadi; Dwi Kartini; Muliadi Muliadi
Jurnal Komputasi Vol. 9 No. 2 (2021)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i2.2864

Abstract

Abstract — Pada kolam pembibitan ikan, pengamatan kualitas air harus terus dilakukan secara berkala karena kondisi pembibitan ikan merupakan masa rawan kematian yang diakibatkan oleh perubahan kondisi lingkungan pembibitan, parameter yang paling berpengaruh dalam kelangsungan hidup ikan yaitu kondisi keasaman air (Ph), kekeruhan air (Turbidiy), oksigen terlarut dalam air (DO) dan suhu air. Parameter tersebut harus selalu dimonitor dan dikontrol untuk mencapai kestabilan lingkungan pembibitan sesuai yang diharapkan. Telah dibuat sistem monitoring dan kontrol terhadap parameter yang berpengaruh pada pembibitan ikan menggunakan sistem fuzzy inferensi. Pengukuran parameter lingkungan dilakukan menggunakan sensor kemudian nilai parameter tersebut disesuaikan dengan nilai fuzzifikasi yang telah dibuat hingga menghasilkan nilai defuzzifikasi, output dari defuzzyfikasi akan melakukan kontrol terhadap parameter tersebut untuk mencapai nilai kestabilan lingkungan air. Pengontrolan Ph dan kekeruhan air dilakukan dengan mengganti air hingga mencapai tinggkat ph dan kejernian air yang sesuai kondisi yang diharapkan, jumlah buangan air dapat dihitung menggunakan teorema fluida. Perhitungan fuzzy dan Pengembangan antarmuka monitoring dan kontrol dibangun menggunakan program berbasis grafik LabView.
Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor Dwi Kartini; Andi Farmadi; Muliadi muliadi; Dodon Turianto Nugrahadi; Pirjatullah Pirjatullah
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2965

Abstract

Pneumonia adalah penyakit menular yang menyerang saluran pernapasan bagian bawah dan merupakan salah satu penyebab utama kematian pada anak-anak di bawah lima tahun. Pneumonia mudah menyerang balita yang disebabkan oleh berbagai mikroorganisme yang ada di lingkungan seperti virus, bakteri, jamur dan bakteri mikro. Penelitian ini menggunakan K-Nearest Neighbor (KNN) untuk klasifikasi pneumonia pada pasien berdasarkan gejala yang dialami. Metode klasifikasi KNN dilakukan dengan membandingkan jarak objek antara data tes dan objek keseluruhan pada data pelatihan berdasarkan data riwayat medis pasien. Perbandingan persentase data pelatihan dan data pengujian yang digunakan adalah 90:10, 80:20, dan 70:30 untuk menghitung nilai jarak terdekat dari data pengujian dengan data pelatihan keseluruhan dengan jumlah k yang digunakan. Matriks kebingungan digunakan untuk mengukur hasil tes klasifikasi Pneumonia untuk balita dengan kombinasi jumlah data pelatihan dan data pengujian pada jumlah nilai k = {1, 3, 5, 7, 9, 11}, akurasi tertinggi, presisi, penarikan, dan nilai ukuran-F diperoleh. 0,86, 0,89, 1, dan 0,91 untuk data pelatihan 90%, 10% data pengujian dengan nilai k = 3.
Perbandingan Ekstraksi Fitur dengan Pembobotan Supervised dan Unsupervised pada Algoritma Random Forest untuk Pemantauan Laporan Penderita COVID-19 di Twitter Sulastri Norindah Sari; Mohammad Reza Faisal; Dwi Kartini; Irwan Budiman; Triando Hamonangan Saragih; Muliadi Muliadi
Jurnal Komputasi Vol. 11 No. 1 (2023)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i1.6650

Abstract

Dimasa sekarang masyarakat sudah berani melaporkan dirinya terpapar COVID-19 melalui unggahan di media sosial seperti Twitter. Hal ini dapat dimanfaatkan oleh masyarakat sekitar atau lembaga kesehatan untuk memberikan bantuan terhadap pelapor. Pemantauan laporan penderita COVID-19 di Twitter dapat dilakukan secara otomatis dengan algoritma machine learning untuk klasifikasi teks. Pada kasus klasifikasi teks, algoritma machine learning menerima input berupa data terstruktur hasil ekstraksi fitur dengan teknik unigram dengan pembobotan. Metode pembobotan kata unsupervised merupakan pembobotan yang tidak memperhatikan letak term di kelas positif atau negatif. Kemudian metode pembobotan ini dikembangkan menjadi pembobotan supervised, karena dalam proses pembobotannya metode ini membobotkan term dengan memperhatikan letak term di kelas positif atau negatif. Pada riset ini dilakukan perbandingan kedua jenis pembobotan pada klasifikasi data tweet gejala covid dengan algoritma machine learning yaitu Random Forest. Dari hasil penelitian didapat hasil kinerja klasifikasi dengan pembobotan supervised Delta TF-IDF terbukti lebih bagus dengan akurasi sebesar 88,5% sedangkan dengan pembobotan unsupervised TF-IDF diperoleh hasil akurasi 87,9%
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Hana, Elvina Nur; Faisal, Mohammad Reza; Kartini, Dwi; Mazdadi, Muhammad Itqan; Saputro, Setyo Wahyu; Indriani, Fatma; Satou, Kenji
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes Siti Fathmah; Dwi Kartini; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21528

Abstract

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.
The Dynamics of Checks and Balances in the New Administration: Safeguarding the Integrity of the Judiciary in Achieving Substantive Justice Kartini, Dwi; Alpiana, Depi Angga; Iristian, Yovan
Lex Journal: Kajian Hukum & Keadilan Vol 10 No 2 (2026): In Progress
Publisher : Faculty of Law, University of Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/lex.v10i2.12036

Abstract

This research examines the critical tension between executive expansion and judicial independence within the context of the 2024 general elections and the emergence of new administrations globally and in Indonesia. Utilizing a normative-legal and comparative approach, the study analyzes the phenomenon of "autocratic legalism," where legal instruments are strategically repurposed to consolidate political power, thereby threatening the traditional framework of checks and balances. The findings indicate a significant global regression in democratic standards, with a 67% "fatality rate" for autocratizing democracies and a measurable decline in judicial constraints on executive power in 61% of nations. In Indonesia, the 2024 transition highlighted structural vulnerabilities in the Constitutional Court, particularly regarding ethical dilemmas and the "judicial capture" of candidate eligibility requirements. The article further explores the paradigm shift from proceduralism to substantive justice, arguing that the realization of equitable outcomes requires judges to adopt a "Justice as Fairness" approach, rooted in natural law and human rights, to balance the inherent power asymmetry between the state and its citizens. The study concludes that safeguarding judicial integrity requires institutional reforms including transparent appointment mechanisms, budgetary autonomy, and a progressive interpretive framework that transcends formalistic legal positivism.
Co-Authors A.A. Ketut Agung Cahyawan W Abadi, Friska Abdullayev, Vugar Adawiyah, Laila Adin Nofiyanto, Adin Ahdyani, Annisa Salsabila Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Ajwa Helisa Al Habesyah, Noor Zalekha Alpiana, Depi Angga Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Antoh, Soterio Arfan Eko Ari Widodo Aryastuti, Nurul Azizah, Siti Roziana Bachtiar, Adam Mukharil Badali, Rahmat Amin Budiman, Irwan Daduk Merdika Mansur Dalimunthe, Gallang Perdhana Deni Kurnia Diana Sari Dike Bayu Magfira, Dike Bayu Dina Arifah Dita Amara Dodon Turianto Nugrahadi Dzira Naufia Jawza Faisal, Mohammad Reza Faisal, Mohammad Reza Fatma Indriani Fatma Indriani Fitra Ahya Mubarok Friska Abadi Hafiz Ilhami Hana, Elvina Nur Helma Herlinda Ihsan, Muhammad Khairi Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Jhondy Baharsyah Jumadi Mabe Parenreng Lestari, Mega Lilies Handayani Lumbanraja, Favorisen R Mafazy, Muhammad Meftah Mahmud Mahmud Maya Yusida Mera Kartika Delimayanti Miftakhul Huda Muhammad Fauzan Nafiz Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muhammad Syahriani Noor Basya Basya Muliadi Muliadi Muliadi Muliadi . Muliadi . Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Muliadi, Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Nor Indrani Nurcahyati, Ica Nurdiansyah Nurdiansyah Nurul Chamidah P., Chandrasekaran Padhilah, Muhammad Pirjatullah Pirjatullah Puput Dani Prasetyo Adi Radityo Adi Nugroho Radityo Adi Nugroho Rahmat Hidayat Rahmat Ramadhani Reina Alya Rahma Riadi, Putri Agustina Rizian, Rizailo Akfa Rizky, Muhammad Hevny Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rusdiani, Husna Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi Salsha Farahdiba Saragih, Triando Hamonangan Sari, Fitri Eka Satou, Kenji Septyan Eka Prastya Setyo Wahyu Saputro Shalehah Siena, Laifansan Siti Aisyah Solechah Siti Fathmah Sulastri Norindah Sari Sule, Ernie Tisnawati Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Vina Maulida, Vina Wahyu Caesarendra Wijaya Kusuma, Arizha Yevis Marty Oesman YILDIZ, Oktay Yovan Iristian Yuyus Suryana