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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) 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) JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM)
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts 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 Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Illiteracy Classification Using K Means-Naïve Bayes Algorithm Muhammad Firman Aji Saputra; Triyanna Widiyaningtyas; Aji Prasetya Wibawa
JOIV : International Journal on Informatics Visualization Vol 2, No 3 (2018)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (675.429 KB) | DOI: 10.30630/joiv.2.3.129

Abstract

Illiteracy is an inability to recognize characters, both in order to read and write. It is a significant problem for countries all around the world including Indonesia. In Indonesia, illiteracy rate is generally set as an indicator to see whether or not education in Indonesia is successful. If this problem is not going to be overcome, it will affect people’s prosperity. One system that has been used to overcome this problem is prioritizing the treatment from areas with the highest illiteracy rate and followed by areas with lower illiteracy rate. The method is going to be a way easier to be applied if it is supported by classification process. Since the classification process needs a class, and there has not been any fine classification of illiteracy rate, there is needed a clustering process before classification process. This research is aimed to get optimal number of classes through clustering process and know the result of illiteracy classification process. The clustering process is conducted by using k means algorithm, and for the classification process is conducted by using Naïve Bayes algorithm. The testing method used to assess the success of classification process is 10-fold method. Based on the research result, it can be concluded that the optimal illiteracy classes are three classes with the classification accuracy value of 96.4912% and error rate value of 3.5088%. Whereas the classification with two classes get the accuracy value of 93.8596% and error rate value of 6.1404%. And for the classification with five classes get the accuracy value of 90.3509% and error rate value of 9.6491%.
Application-Level Caching Approach Based on Enhanced Aging Factor and Pearson Correlation Coefficient Zulfa, Mulki Indana; Maryani, Sri; Ardiansyah, -; Widiyaningtyas, Triyanna; Ali, Waleed
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2143

Abstract

Relational database management systems (RDBMS) have long served as the fundamental infrastructure for web applications. Relatively slow access speeds characterize an RDBMS because its data is stored on a disk. This RDBMS weakness can be overcome using an in-memory database (IMDB). Each query result can be stored in the IMDB to accelerate future access. However, due to the limited capacity of the server cache in the IMDB, an appropriate data priority assessment mechanism needs to be developed. This paper presents a similar cache framework that considers four data vectors, namely the data size, timestamp, aging factor, and controller access statistics for each web page, which serve as the foundation elements for determining the replacement policy whenever there is a change in the content of the server cache. The proposed similarCache employs the Pearson correlation coefficient to quantify the similarity levels among the cached data in the server cache. The lowest Pearson correlation coefficients cached data are the first to be evicted from the memory. The proposed similarCache was empirically evaluated based on simulations conducted on four IRcache datasets. The simulation outcomes revealed that the data access patterns, and the configuration of the allocated memory cache significantly influenced the hit ratio performance. In particular, the simulations on the SV dataset with the most minor memory space configuration exhibited a 2.33% and 1% superiority over the SIZE and FIFO algorithms, respectively. Future tasks include building a cache that can adapt to data access patterns by determining the standard deviation. The proposed similarCache should raise the Pearson coefficient for often available data to the same level as most accessed data in exceptional cases.
Addressing Class Imbalance of Health Data: A Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies Hairani, Hairani; Widiyaningtyas, Triyanna; Dwi Prasetya, Didik
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.8.3.2283

Abstract

The Synthetic Minority Oversampling Technique (SMOTE) method is the baseline for solving unbalanced data problems. The working concept of the SMOTE method is to generate new synthetic data patterns by performing linear interpolation between minority class samples based on k-nearest neighbors. However, the SMOTE method has weaknesses, namely the problem of overgeneralization due to excessive sampling of sample noise and increased overlapping between classes in the decision boundary area, which has the potential for noise data. Based on the weaknesses of the Smote method, the purpose of this research is to conduct a systematic literature review on the Smote method modification approach in solving unbalanced data. This systematic literature review method comprises keyword identification, article search process, determination of selection criteria, and selection results based on criteria. The results of this study showed that the SMOTE modification approach was based on filtering, clustering, and distance modification to reduce the resulting noise data. The filtering approach removed the noise data before SMOTE, positively impacting resolving unbalanced data. Meanwhile, the use of a clustering approach in SMOTE can minimize the overlapping artificial minority data that has noise potential. The most used datasets are Pima 60% and Haberman 50%. The most used performance evaluation on unbalanced data is f1-measure 57%, accuracy 55%, recall 43%, and AUC 27%. The implication of the results of this literature review is to provide opportunities for further research in modifying SMOTE in addressing health data imbalances, especially handling noise and overlapping data. The thoroughness of our literature review should instill confidence in the research community.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Adam Ramadhani P Adiba Qonita Ahmad Farobi Ahmad Fuadi Aji P Wibawa Aji Prasetya Wibawa Ali, Waleed 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 Falah, Moh Zainul 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 Maryani, Sri Moh Zainul Falah Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nafalski, Andrew Nurhidayati Pindo Tutuko Poppy Indriana Pratama, Satria Putra Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Rendy Yani Susanto Rhomdani, Rohmad Wahid Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Setiadi Cahyono Putro Shandy Krisnawan Soenar Soekopitojo Soraya Norma Mustika Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma