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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) 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 Journal of Embedded Systems, Security and Intelligent Systems Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Dwi Kartini, Dwi
Unknown Affiliation
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 Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation

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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%
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.
Feature extraction and machine learning methods for biometric recognition based on fusion of ECG and fingerprint Hafiz Ilhami; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Irwan Budiman; Andi Farmadi; Dwi Kartini; Puput Dani Prasetyo Adi; Jumadi Mabe Parenreng
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10541

Abstract

This research introduces a multimodal biometric authentication framework by amalgamating electrocardiogram (ECG) and fingerprint modalities through the utilization of diverse feature extraction methodologies and machine learning classifiers. The proposed methodology aspires to augment precision and mitigate spoofing vulnerabilities in contrast to traditional single-modality systems. Among the feature extraction techniques assessed—grayscale, binary, Sobel edge detection, and minutiae—Naïve Bayes (NB) in conjunction with minutiae features exhibited superior performance, attaining an accuracy rate of 96.25%. Supplementary experiments employing random forest (RF) and support vector machine (SVM) also revealed commendable classification efficacy, underscoring the robustness of the fusion methodology. This investigation provides a pragmatic and secure biometric framework by harnessing complementary biometric characteristics to enhance authentication dependability. The proposed system presents promising applications in real-world contexts, particularly concerning mobile security and healthcare access control. Future research endeavors will tackle challenges associated with ECG signal variability, computational efficiency, and extensive deployment.
Application of SMOTE to Handle Imbalance Class in Deposit Classification Using the Extreme Gradient Boosting Algorithm Dina Arifah; Triando Hamonangan Saragih; Dwi Kartini; Muliadi Muliadi; Muhammad Itqan Mazdadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26155

Abstract

Deposits became one of the main products and funding sources for banks and increasing deposit marketing is very important. However, telemarketing as a form of deposit marketing is less effective and efficient as it requires calling every customer for deposit offers. Therefore, the identification of potential deposit customers was necessary so that telemarketing became more effective and efficient by targeting the right customers, thus improving bank marketing performance with the ultimate goal of increasing sources of funding for banks. To identify customers, data mining is used with the UCI Bank Marketing Dataset from a Portuguese banking institution. This dataset consists of 45,211 records with 17 attributes. The classification algorithm used is Extreme Gradient Boosting (XGBoost) which is suitable for large data. The data used has a high-class imbalance, with "yes" and "no" percentages of 11.7% and 88.3%, respectively. Therefore, the proposed solution in the research, which focused on addressing the Imbalance Class in the Bank marketing dataset, was to use Synthetic Minority Over-sampling (SMOTE) and the XGBoost method. The result of the XGBoost study was an accuracy of 0.91016, precision of 0.79476, recall of 0.72928, F1-Score of 0.56198, ROC Area of 0.93831, and AUCPR of 0.63886. After SMOTE was applied, the accuracy was 0.91072, the precision was 0.78883, the recall was 0.75588, F1-Score was 0.59153, ROC Area was 0.93723, and AUCPR was 0.63733. The results showed that XGBoost and SMOTE could outperform other algorithms such as K-Nearest Neighbor, Random Forest, Logistic Regression, Artificial Neural Network, Naïve Bayes, and Support Vector Machine in terms of accuracy. This study contributes to the development of effective machine learning models that can be used as a support system for information technology experts in the finance and banking industries to identify potential customers interested in subscribing to deposits and increasing bank funding sources.
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Muhammad Fauzan Nafiz; Dwi Kartini; Mohammad Reza Faisal; Fatma Indriani; Triando Hamonangan Saragih
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Automatic Analysis of Natural Disaster Messages on Social Media Using IndoBERT and Multilingual BERT Yasmin Dwi Safitri; Mohammad Reza Faisal; Dwi Kartini; Triando Hamonangan Saragih; Friska Abadi; Adam Mukharil Bachtiar
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3140

Abstract

Information about natural disasters disseminated through social media can serve as an important data source for mitigation processes and early warning systems. Social media platforms, such as X (formerly known as Twitter), have become primary channels for conveying real-time information, especially during disaster emergencies. With the large amount of unstructured disaster-related text that must be processed, the main challenge is accurately filtering and classifying messages into three categories: eyewitness, non-eyewitness, and don’t know. This research aims to compare the performance of four BERT-based natural language processing models, namely IndoBERT, IndoBERT with Masked Language Modeling (MLM), Multilingual BERT, and Multilingual BERT with MLM, in classifying Indonesian-language disaster messages. The dataset used in this study was obtained from previous research and publicly available data on GitHub, consisting of annotated messages related to floods, earthquakes, and forest fires. The method applied is a deep learning approach using the hold-out technique with an 80:20 ratio for training and testing data, and the same ratio applied to split the training data into training and validation subsets, with stratification to maintain balanced class proportions. In addition, variations in batch size were explored to evaluate their effect on model performance stability. The results show that the IndoBERT model achieved the highest performance on the flood and earthquake datasets, with accuracies of 80.67% and 81.50%, respectively. Meanwhile, IndoBERT with MLM pre-training recorded the highest accuracy on the forest fire dataset, 88.33%. Overall, IndoBERT demonstrated the most consistent and superior performance across datasets compared to the other models. These findings indicate that IndoBERT has strong capabilities in understanding Indonesian disaster-related text, and the results can be used as a foundation for developing automatic classification systems to support real-time disaster monitoring and early warning applications
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Elvina Nur Hana; Mohammad Reza Faisal; Dwi Kartini; Muhammad Itqan Mazdadi; Setyo Wahyu Saputro; Fatma Indriani; Kenji Satou
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%.
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.
Co-Authors A.A. Ketut Agung Cahyawan W Abdullayev, Vugar Adam Mukharil Bachtiar Adawiyah, Laila Adin Nofiyanto, Adin Ahdyani, Annisa Salsabila Ahmad Balya Al Erpat Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Ajwa Helisa Al Habesyah, Noor Zalekha Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Antoh, Soterio Arfan Eko Ari Widodo Aryastuti, Nurul Azizah, Siti Roziana 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 Elvina Nur Hana Faisal, Mohammad Reza Faisal, Mohammad Reza Fatma Indriani Fatma Indriani Fitra Ahya Mubarok Friska Abadi Hafiz Ilhami 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 Kenji Satou Lestari, Mega Lilies Handayani Lumbanraja, Favorisen R Mafazy, Muhammad Meftah Mahmud Mahmud Maya Yusida Mera Kartika Delimayanti Miftakhul Huda Muhammad Fattah Edric Camilo 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 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 Said, Muhammad Al Ichsan Nur Rizqi Salsha Farahdiba Saragih, Triando Hamonangan Sari, Fitri Eka 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 Yasmin Dwi Safitri Yevis Marty Oesman YILDIZ, Oktay Yuyus Suryana