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All Journal Biota: Jurnal Ilmiah Ilmu-Ilmu Hayati Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi Scientific Journal of Informatics Profesi Pendidikan Dasar Journal of Information Systems Engineering and Business Intelligence Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Penelitian Pendidikan IPA (JPPIPA) Register Journal Adi Widya : Jurnal Pengabdian Masyarakat JURNAL SINEKTIK BASIS (BAHASA DAN SASTRA INGGRIS) JCES (Journal of Character Education Society) Diglosia: Jurnal Kajian Bahasa, Sastra, dan Pengajarannya English Language and Literature International Conference (ELLiC) Proceedings JURNAL PENDIDIKAN TAMBUSAI Jurnal Basicedu Journal of Pragmatics Research Jurnal Komunikasi Pendidikan JURNAL PENDIDIKAN MIPA BENING Community Development Journal: Jurnal Pengabdian Masyarakat Randwick International of Education and Linguistics PIPER Journal of Soft Computing Exploration Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An Jurnal Basicedu Studies in English Language and Education Surakarta English and Literature Journal Journal of Pragmatics Research Journal of Student Research Exploration Journal of Information System Exploration and Research DIANKES : Jurnal Pengabdian Teknologi Informasi dan Kesehatan Journal of Linguistics, Culture and Communication Interling : International Journal of English Language Teaching, Literature and Linguistics Jurnal Abdi Negeri
Jumanto Jumanto, Jumanto
Faculty of Humanities, Dian Nuswantoro University
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Journal : Journal of Soft Computing Exploration

 1 
Implementation of fuzzy tsukamoto in employee performance assessment Dewi, Meilina Taffana; Zaaidatunni'mah, Untsa; Al Hakim, M. Faris; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.52

Abstract

Employees are one of the important things for the sustainability of a company, because employees are company assets. In addition, employee performance is also something that cannot be ignored because it determines the achievement of company goals. So it is important to monitor employee performance and conduct performance appraisals. With the addition of performance appraisal, the company can determine the provision of rewards, promotions, and punishments. It can be used as a work evaluation stage to improve the quality of work. Employee performance appraisal is based on several predetermined criteria, including responsibility, discipline, and attitude which in the end results in between two linguistic values, namely good or bad. One method for evaluating employee performance is the Tsukamoto fuzzy method. With the Tsukamoto fuzzy method, it is hoped that the assessment can be carried out fairly and measurably.
Room occupancy classification using multilayer perceptron Wijaya, Dandi Indra; Aulia, Muhammad Kahfi; Jumanto, Jumanto; Hakim, M. Faris Al
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.53

Abstract

A room that should be comfortable for humans can create a sense of absence and appear diseases and other health problems. These rooms can be from boarding rooms, hotels, office rooms, even hospital rooms. Room occupancy prediction is expected to help humans in choosing the right room. Occupancy prediction has been evaluted with various statistical classification models such as Linier Discriminat Analysis LDA, Classification And Regresion Trees (CART), and Random Forest (RF). This study proposed learning approach to classification of room occupancy with multi layer perceptron (MLP). The result shows that a proper MLP tuning paramaters was able estimate the occupancy with 88.2% of accuracy
Rainfall prediction in Blora regency using mamdani's fuzzy inference system Damayanti, Dela Rista; Wicaksono, Suntoro; Hakim, M. Faris Al; Jumanto, Jumanto; Subhan, Subhan; Ifriza, Yahya Nur
Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i1.69

Abstract

In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
Optimization of breast cancer classification using feature selection on neural network Jumanto, Jumanto; Mardiansyah, M Fadil; Pratama, Rizka Nur; Hakim, M. Faris Al; Rawat, Bibek
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.78

Abstract

Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning Jumanto, Jumanto; Nugraha, Faizal Widya; Harjoko, Agus; Muslim, Much Aziz; Alabid, Noralhuda N.
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.99

Abstract

Glaucoma is an eye disease that is the second leading cause of blindness. Examination of glaucoma by an ophthalmologist is usually done by observing the retinal image directly. Observations from one doctor to another may differ, depending on their educational background, experience, and psychological condition. Therefore, a glaucoma detection system based on digital image processing is needed. The detection or classification of glaucoma with digital image processing is strongly influenced by the feature extraction method, feature selection, and the type of features used. Many researchers have carried out various kinds of feature extraction for glaucoma detection systems whose accuracy needs to be improved. In general, there are two groups of features, namely morphological features and non-morphological features (image-based features). In this study, it is proposed to detect glaucoma using texture features, namely the GLCM feature extraction method, histograms, and the combined GLCM-histogram extraction method. The GLCM method uses 5 features and the Histogram uses 6 features. To distinguish between glaucoma and non-glaucoma eyes, the multi-layer perceptron (MLP) artificial neural network model serves as a classifier. The data used in this study consisted of 136 fundus images (66 normal images and 70 images affected by glaucoma). The performance obtained with this approach is an accuracy of 93.4%, a sensitivity of 86.6%, and a specificity of 100%.
Ensemble learning technique to improve breast cancer classification model Dullah, Ahmad Ubai; Apsari, Fitri Noor; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.166

Abstract

Cancer is a disease characterized by abnormal cell growth and is not contagious, such as breast cancer which can affect both men and women. breast cancer is one of the cancer diseases that is classified as dangerous and takes many victims. However, the biggest problem in this study is that the classification method is low and the resulting accuracy is less than optimal. the purpose of this study is to improve the accuracy of breast cancer classification. Therefore, a new method is proposed, namely ensemble learning which combines logistic regression, decision tree, and random forest methods, with a voting system. This system is useful for finding the best results on each parameter that will produce the best prediction accuracy. The prediction results from this method reached an accuracy of 98.24%. The resulting accuracy rate is more optimal by using the proposed method.
Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding Ningsih, Maylinna Rahayu; Wibowo, Kevyn Aalifian Hernanda; Dullah, Ahmad Ubai; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.193

Abstract

The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.
Co-Authors Ade Parlaungan Nasution, Ade Parlaungan Adi, Yogi Kuncoro Agus Harjoko Agustiani, Agustiani Al Hakim, M. Faris Alabid, Noralhuda N. Alamsyah - Anggit Grahito Wicaksono Apsari, Fitri Noor Ari Widodo Ascarya, Farrel Aulia, Muhammad Kahfi Aurelia, Bening Febri Badriyah, Fitria Nurul Budi Prasetiyo, Budi Chairunnisa, Tsania Damayanti, Dela Rista Dewi, Meilina Taffana Dullah, Ahmad Ubai Dwi Anggraeni Dwi Eko Waluyo DWI HAPSORO Ema Butsi Prihastari Ratna Widyaningrum, Ema Butsi Prihastari Endang Sugiharti, Endang Faizal Risdianto Fatahillah, Dimas Ferdiansyah, Dicky Feri Faila Sufa, Feri Faila HAJRIAL ASWIDINNOOR Hakim, M. Faris Al Hanafi Hanafi Handayani, Sri Haryati Sulistyorini Hendra Kurniawan Herlina Kurniawati Hidayat Hidayat Ilham Maulana Irmade, Oka Jati, Ismail Wahyu Khalifah, Viera Nur Khoirunnisa, Avicenia Nasywa KURNIATI, SRI AYU Kusuma, Novelia Salsa Dara Lestari, Apri Dwi Lintang, Irendra Luh Titi Handayani, Luh Titi Lutfiyah, Nur Indah Sulistyowati Machfudz, Machfudz Mardiansyah, M Fadil Marshanda, Putri Martha Yohana Sinaga Masa, Amin Padmo Azam Mellisa, Mellisa Minghat, Asnul Dahar Bin Much Aziz Muslim Mukarom, Rosyid Fadhil Al Mulyana, Aditya Fajar Mulyaningtyas, Lintang Ayu Murtafiah, Eni Muzayanah, Rini Nikmah, Tiara Lailatul Nina Fitriani, Nina Ningsih, Maylinna Rahayu Nugraha, Faizal Widya NUGROHO, MUHAMMAD IRFAN Pertiwi, Dwika Ananda Agustina Prabaswara, Ireneus Prabowo Yudo Jayanto Pratama, Rizka Nur Prihatsari, Ema Butsi Puji Purwatiningsih, Aris Pulung Nurtantio Andono Puspita, Wiyanda Putri, Chindi Dwi Ayu Prabowo Putri, Salma Aprilia Huda Raden Arief Nugroho Rahayu, Emik Rahmanti Asmarani Ramadhan, Taufiq Brahmantyo Lintang ramayanti, ismarita Rawat, Bibek Rianto, Nur Aziz Kurnia Riza Arifudin Rizkasari, Elinda Rofik Rofik, Rofik RUSMILAH SUSENO Sa'ud, Udin Syaefudin Sagimin, Eka Margianti Salsabila, Halimah Sam’an, Muhammad Sarafuddin, Sarafuddin Sinaga, Markus Soehendro, Eunike Imanuela Subhan Subhan Subrata, Monika Rosalia Sudarsono Sugiaryo, Sugiaryo Syamsu Rizal, Sarif Tanzilal Mustaqim Wahyu Sopandi Wibowo, Kevyn Aalifian Hernanda Wibowo, Kevyn Alifian Hernanda Wicaksono, Suntoro Widiaswara, R. Anantama Widyasari, Alma Wijaya, Dandi Indra Yahya Nur Ifriza Yosza Dasril Yulianingsih, Ratri Zaaidatunni'mah, Untsa