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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Reconfigurable and Embedded Systems (IJRES) Semantik Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Abdi Insani Madani : Indonesian Journal of Civil Society Journal of Applied Data Sciences Prosiding Konferensi Nasional PKM-CSR eProceedings of Applied Science eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Jurnal Rekayasa elektrika Jurnal INFOTEL
Indrarini Dyah Irawati
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Comparative Analysis of Imputation Methods for Enhancing Predictive Accuracy in Data Models Zamri, Nurul Aqilah; Jaya, M. Izham; Irawati, Indrarini Dyah; Rassem, Taha H.; Rasyidah, -; Kasim, Shahreen
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1666

Abstract

The presence of missing values within datasets can introduce a detrimental bias, significantly impeding the predictive algorithm's ability to discern patterns and accurately execute prediction. This paper aims to elucidate the intricacies of data imputation methods, providing a more profound understanding of prevalent imputation methods, including list-wise deletion (IGN), mean imputation (AVG), K-Nearest Neighbors (KNN), MissForest (MF), and Predictive Mean Matching (PMM). The dataset employed in this study consists of financial data about S&P 500 companies in the Compustat North America database. The training and validation dataset encompasses 1973 instances, consisting of data during the fourth quarter of 2009, the first quarter of 2010, and the third quarter of 2014. Within this set, 457 missing values were identified and imputed. The test dataset comprises 197 randomly selected instances from the fourth quarter of 2014, equivalent to ten percent of the total instances in the training dataset. The evaluation findings prominently position the dataset derived from MF imputation as the leading performer among all the imputed datasets. The insights derived from this study are intended to assist practitioners in making informed choices when selecting the most suitable data imputation method, particularly in the context of predictive modeling tasks.
Analysis of Pneumonia from Chest X-Ray Images Using an Optimized Ensemble Machine Learning Models with Voting Classifier Monita, Vivi; Hanan Lutfianto, Naufal; Dyah Irawati, Indrarini
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3689

Abstract

Pneumonia is a pulmonary disease resulting from infections caused by bacteria, viruses, or fungi that invade the lungs. This condition leads to inflammation due to the accumulation of fluids, blood cells, and other substances in the alveoli. Common symptoms experienced by patients include fever, coughing, and production of phlegm. Although pneumonia can affect individuals of any age, those with weakened immune systems are particularly vulnerable. Children and elderly individuals are especially prone to contracting this illness. The present research employs an ensemble learning approach for pneumonia detection using chest x-ray images to address this issue, specifically integrating support vector machines (SVMs) and random forests (RFs). The primary aim is to evaluate the effectiveness of ensemble learning through a voting classifier in improving pneumonia detection accuracy compared to individual machine learning models. The methodology includes preprocessing the data with contrast-limited adaptive histogram equalization (CLAHE), which minimizes noise by defining a kernel matrix and substituting each pixel's intensity with the weighted average of its neighboring pixels and itself. The research also involves training models using SVM and RF algorithms with hyperparameter optimization. These individual models are then assessed and compared using performance metrics such as accuracy, area under the curve (AUC), specificity, sensitivity, confusion matrix, and computational efficiency. By harnessing the strengths of ensemble learning, this research aims to contribute to the development of reliable pneumonia detection systems, with potential applications in clinical environments where timely and accurate diagnosis is essential for patient management. This research achieved 99.40% and 96.32% accuracy, 99.97% and 96.52% AUC, and 0.0436% and 0.0451% loss. This method tackles others that use deep learning and single machine learning with all balanced datasets.
Co-Authors ., Ridwan A. V. Senthil Kumar Abi Hakim Amanullah Adi Arief Wicaksono ADIANGGIALI, ANYELIA Afandi, Mas Aly Akhmad Alfaruq Akhmad Hambali Alfaruq, Akhmad Andri Juli Setiawan Anggun Fitrian Isnawati Anwar Muqorobin Aprilia, Rizky Arfianto Fahmi Arif Indra Irawan ARIS HARTAMAN Ary Nugroho, Bambang Asep Mulyana Ayu Irmawati Bagus Budi Wibowo Bayu Erfianto Dadan Nur Ramadan Didi Supriyadi Dzikri Fajduani, Fazrian Ezi Rohmat Fadilla , Rahma Fairuz Azmi Fajrul Falaah, Alif Fandi Fachrulrozi, Muhammad Gabriel Sabadtino Siahaan Gelar Budiman Gita Indah Hapsari Hadjwan, Razel Hafidudin . Hanan Lutfianto, Naufal Ibnu Syahban M, Novaldi Inung Wijayanto Istikmal Ivosierra Andrea Larasaty Jaya, M. Izham Justisia Satiti Larasaty, Ivosierra Andrea LATIP, ROHAYA Leanna Vidya Yovita Lenna Vidya Yovita Lionel Saonard, Aldo Lutvi Murdiansyah Murdiansyah Maidin, Siti Sarah Miftahul Khairat Sukma Muh. Kurniawan, A. Muhamad Roihan Muhammad Dimas Arfianto Muhammad Dimas Arfianto, Muhammad Dimas Muhammad Iqbal Musyaffa, Nadhif Athallah Natia Pradnyaswari, Luh Gede Nita Laananila, Grace Nur Ramadhan, Dadan Nurwan Reza Fachrurrozi Nyoman Karna, Nyoman Paundra Aldila Pradana, Gde Agus Wira Satria Pradika Caesarizky Kurniahadi Prayoga, Andry Priawan, Agi Rahmafadilla, Rahmafadilla Ramadhan, adan Nur Ramdani, Ahmad Zaky Rassem, Taha H. Rasyidah, - Rendy Munadi Reni Dyah Wahyuningrum Ridha Muldina Negara Ridwan . Rita Purnamasari Rizal, Mochammad Fahru Rizky Aulia Rahman ROHMAT TULLOH Roykhan Sukma, Hanif Sandova, Fisal Oktafian Penta Sandy Purniawan Santosa, Harjono Priyo Sasmi Hidayatul Yulianing Tyas Shahreen Kasim, Shahreen Shiddiq, Rama Wijaya Silvia, Helen Siti Sarah Maidin Siti Zahrotul Fajriyah Sofia Naning Hertiana Suci Alfi Syahri Tune, Andi Suci Aulia Sugeng Santoso Sugondo Hadiyoso Susi Susanti Suyatno Suyatno Syifa Nurgaida Yutia Tasya Chairunnisa Tita Haryanti Triasari, Biyantika Emili Uwais Razaqtana, Muhammad Vivi Monita Wartingrum, Nadia Wijanarko, Sulistyo Yudha Purwanto Yudiansyah Yudiansyah YULI SUN HARIYANI Zamri, Nurul Aqilah Zero Fomandes, Muhammad Zhao, Zhong