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All Journal Biota: Jurnal Ilmiah Ilmu-Ilmu Hayati Telematika : Jurnal Informatika dan Teknologi Informasi Profesi Pendidikan Dasar Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Adi Widya : Jurnal Pengabdian Masyarakat JURNAL SINEKTIK BASIS (BAHASA DAN SASTRA INGGRIS) JCES (Journal of Character Education Society) English Language and Literature International Conference (ELLiC) Proceedings Jurnal Basicedu Jurnal Komunikasi Pendidikan JURNAL PENDIDIKAN MIPA BENING 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 Student Research Exploration Journal of Information System Exploration and Research DIANKES : Jurnal Pengabdian Teknologi Informasi dan Kesehatan Interling : International Journal of English Language Teaching, Literature and Linguistics
Jumanto Jumanto, Jumanto
Faculty of Humanities, Dian Nuswantoro University
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PENINGKATAN PRODUKSI CABAI RAWIT (Capsicum frutescens L.) MELALUI PEMBERIAN PUPUK ORGANIK CAIR (POC) BATANG PISANG Yulianingsih, Ratri; Sinaga, Markus; Kurniawati, Herlina; Jumanto, Jumanto
Publikasi Informasi Pertanian Vol 20 No 2 (2024): PIPER
Publisher : Universitas Kapuas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51826/piper.v20i2.1316

Abstract

Produksi cabai rawit di Kabupaten Sintang masih rendah, yaitu 2,96 ton/ha. Salah satu penyebab rendahnya produksi cabai rawit adalah rendahnya kandungan bahan organik dalam tanah PMK. Kekurangan bahan organik tersebut dapat diatasi dengan pemberian pupuk organik cair batang pisang. Tujuan penelitian ini untuk mengetahui peningkatan produksi cabai rawit melalui pemberian pupuk organik cair batang pisang. Penelitian ini menggunakan Rancangan Acak Kelompok (RAK) dengan 5 taraf perlakuan yaitu P0 =  Kontrol (tanpa pemberian POC batang pisang), P1 = 200 ml POC batang pisang per liter air; P2 = 400 ml liter air per liter air; P3 = 600 ml POC batang pisang per liter air; P4 = 800 ml POC batang pisang per liter air. Parameter yang diamati adalah tinggi tanaman, jumlah buah, dan berat buah. Data dianalisis dengan Analisis Sidik Ragam dan Uji Beda Nyata Jujur (BNJ) pada selang kepercayaan 95% dan 99%. Hasil penelitian dan analisa data menunjukkan bahwa pemberian POC batang pisang berpengaruh nyata terhadap pertumbuhan dan hasil cabai rawit. Pemberian POC batang pisang sebanyak 800 ml per liter air memberikan rata-rata pertumbuhan dan hasil tertinggi, ditunjukkan dengan tinggi tanaman 32,65 cm, rata-rata jumlah buah 18,98, dan rata-rata berat buah 60,89 gram.
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.
MORPHOLOGICAL ANALYSIS OF COMPOUNDING IN THE NOVEL THE BURNING MAZE: THE TRIALS OF APOLLO BY RICK RIORDAN Mulyaningtyas, Lintang Ayu; Khoirunnisa, Avicenia Nasywa; Kusuma, Novelia Salsa Dara; Jumanto, Jumanto
BASIS (Bahasa dan Sastra Inggris) Vol 11 No 2 (2024): JURNAL BASIS UPB
Publisher : Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/basisupb.v11i2.8723

Abstract

This research concerned a morphological analysis of compounding within The Burning Maze: The Trials of Apollo by Rick Riordan. This study morphologically described the importance of language in conveying deep emotion and thematic richness. This descriptive qualitative linguistic research employed the observation and auto-expert judgment methods for the data collection and morphological note-taking techniques for the data analysis. The results revealed patterns of word combinations that contributed to the linguistic richness and depth of the themes. The discussion interpreted these findings in the context of the novel’s storytelling, providing further understanding of the role of morphology in character development and plot complexity. The results showed that compounding in The Burning Maze: The Trials of Apollo by Rick Riordan had a combination of elements with the form of the Noun (N) + Verb (V); Noun (N) + Noun (N); Verb (V) + Verb (V); Adjective (Adj) + Noun (N); Verb (V) + Noun (N); Adverb (Adv) + Noun (N); Noun (N) + Adverb (Adv); Adjective (Adj) + Verb (V); Adjective (Adj) + Adjective (Adj); Adverb (Adv) + Adjective (Adj); Verb (V) + Adverb (Adv); Noun (N) + Adjective(Adj); Verb (V) + Adjective (Adj). Besides revealing linguistic nuances in literary works, this research has strived to deepen our understanding of the intricate layers within literature, showcasing how the careful selection of words enhanced the reader’s immersive journey into the literary landscape.
Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest Jumanto, Jumanto; Muslim, Much Aziz; Dasril, Yosza; Mustaqim, Tanzilal
Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v1i1.104

Abstract

This study conducted a sentiment analysis of the impact of the Covid-19 pandemic in the economic sector on people's lives through social media Twitter. The analysis was carried out on 23,777 tweet data collected from 13 states in Malaysia from 1 December 2019 to 17 June 2020. The research process went through 3 stages, namely pre-processing, labeling, and modeling. The pre-processing stage is collecting and cleaning data. Labeling in this study uses Vader sentiment polarity detection to provide an assessment of the sentiment of tweet data which is used as training data. The modeling stage means to test the sentiment data using the random forest algorithm plus the extraction count vectorizer and TF-IDF features as well as the N-gram selection feature. The test results show that the polarity of public sentiment in Malaysia is predominantly positive, which is 11,323 positive, 4105 neutral, and 8349 negative based on Vader labeling. The accuracy rate from the random forest modeling results was obtained 93.5 percent with TF-IDF and 1 gram.
Co-Authors Ade Parlaungan Nasution, Ade Parlaungan Adi, Yogi Kuncoro Agus Harjoko Agustiani, Agustiani Al Hakim, M. Faris Alabid, Noralhuda N. Anggit Grahito Wicaksono Apsari, Fitri Noor Ari Widodo Ascarya, Farrel Aulia, Muhammad Kahfi Aurelia, Bening Febri 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 Fatahillah, Dimas Feri Faila Sufa, Feri Faila HAJRIAL ASWIDINNOOR Hakim, M. Faris Al Haryati Sulistyorini Hendra Kurniawan Herlina Kurniawati Hidayat Hidayat Ilham Maulana Irmade, Oka Jati, Ismail Wahyu Khoirunnisa, Avicenia Nasywa KURNIATI, SRI AYU Kusuma, Novelia Salsa Dara Lintang, Irendra Luh Titi Handayani, Luh Titi Mardiansyah, M Fadil Marshanda, Putri Martha Yohana Sinaga Masa, Amin Padmo Azam Mellisa, Mellisa Minghat, Asnul Dahar Bin Much Aziz Muslim Mulyaningtyas, Lintang Ayu Ningsih, Maylinna Rahayu Nugraha, Faizal Widya NUGROHO, MUHAMMAD IRFAN Pertiwi, Dwika Ananda Agustina Prabaswara, Ireneus Pratama, Rizka Nur Prihatsari, Ema Butsi Puji Purwatiningsih, Aris Pulung Nurtantio Andono Putri, Chindi Dwi Ayu Prabowo Putri, Salma Aprilia Huda Raden Arief Nugroho Ramadhan, Taufiq Brahmantyo Lintang ramayanti, ismarita Rawat, Bibek RUSMILAH SUSENO Sarafuddin, Sarafuddin Sinaga, Markus Subhan Subhan Sudarsono Tanzilal Mustaqim Wibowo, Kevyn Aalifian Hernanda Wibowo, Kevyn Alifian Hernanda Wicaksono, Suntoro Wijaya, Dandi Indra Yahya Nur Ifriza Yosza Dasril Yulianingsih, Ratri Zaaidatunni'mah, Untsa