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All Journal JURNAL ILMIAH INFORMATIKA Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) bit-Tech Jurnal Sisfotek Global INFOKUM Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika
Wira Hadinata, Wira
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering

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DETERMINATION OF LAND TO POND RATIO IN RAIN WATER HARVESTING SYSTEM TO SUPPORT RICE-SOYBEAN CROPPING PATTERN Hadinata, Wira
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 4, No 1 (2015)
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (662.223 KB)

Abstract

The rain water harvesting system consists of a land area cultivated with rice and soybean cropping patternannually, and a rainwater collection pond. Surpluswater (runoff) in raining season is captured and collected inthe pond, and used for irrigation in the following cultivation. The objective of this researchwas to determine theoptimumratio of the landto pond area. This researchwas carriedout in the IntegratedField Laboratory, Facultyof Agriculture, University of Lampung by using data of soil physical properties (water content, field capacity,permanentwilting point, percolation); rice crop coefficient, soybean crop coefficient and climatological data for13 years from1999 to 2011. Datawas processed using asimulation program(Visual Simulation)presented inthegraphical form. The results showed that the rainwater potential that can be utilized as an alternative irrigationis abundant, about 1500 mm/year - 3000 mm/year with a total of rainwater reaching 314.509,78 m3 over 13years. Based on the simulation, the most effective period of planting, for rice is in January and for soybean is inMay. In addition, the optimum pond dimension to serve 1 ha cropping land is about 2450 m2 in with 3m depth,or the ratio of land to pond is 4:1.Keywords : Evapotranspiration,Pond,Rainwater harvesting,Rice and Soybean
Comparison of Apriori and Frequent Pattern Growth Algorithm in Predicting The Sales of Goods Hadinata, Wira; Waruwu, Jurisman; Hermanto, Toto
JURNAL SISFOTEK GLOBAL Vol 11, No 2 (2021): JURNAL SISFOTEK GLOBAL
Publisher : STMIK Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v11i2.390

Abstract

The increasing number of bona fide companies, especially in the world of retail minimarkets, PT. Suka Maju innovates to make a company that develops in the retail sector so that it can serve consumers well. With the problems - problems in the company PT. Suka Maju still applies unrelated items so that consumers find it difficult to buy related products. PT. Suka Maju does not apply interrelated items such as coffee and sugar, sauce and noodles, bread and cheese. company PT. Suka Maju must act as quickly as possible and requires data analysis using Market Basket Analysis. The purpose of the existence of data in every transaction of product sales to consumers, data can be processed properly to provide information to companies so that transaction data in every product purchase can be useful and to determine the layout of a product. To deal with this problem, researchers found a pattern that can improve a layout pattern or display of sales items in the retail world, one of which is by utilizing product sales transaction data used to support and find an association rule data mining method technique, comparing the algorithm Apriori and algorithm Frequent Pattern Growth. The purpose of this study is to compare 2 algorithms and choose a better algorithm to help find products that are often purchased together. From the results of the research from 10,005 transactions of 27 attributes using the algorithms Apriori and algorithms Frequent Pattern Growth with the minimum parameters of support = 100, confidence = 100 and lift = 2.58, the algorithm Frequent Pattern Growth has the highest accuracy compared to the algorithm Apriori. In the results of this study, it can be said that the algorithm Frequent Pattern Growth is the best for determining interrelated
IMPLEMENTASI NATURAL LANGUAGE PROCESSING PADA CHATBOT UNTUK LAYANAN INFORMASI WISATA (STUDI KASUS: TANGERANG RAYA) Hadinata, Wira; Stianingsih, Lilis
JURNAL ILMIAH INFORMATIKA Vol 12 No 02 (2024): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v12i02.9279

Abstract

The development of computer-based information technology is so fast that it has made many changes in human life. One of the latest technological developments that is becoming the center of attention is artificial intelligence. With this artificial intelligence, computers can perform certain tasks like humans do, namely chatting (chatbot). Chatbot is a computer program that can handle and respond to conversations through writing. The tourism sector is one area that has the potential to grow the economy of a region. Tourism is closely related to the use of information technology. Tourism in Greater Tangerang has the potential to be visited by many tourists. However, the development of existing tourism information is still considered to be less effective and informative. In conveying information, it is still done manually, namely from information heard by other people. Therefore, researchers are trying to develop a chatbot application as a helpdesk using a Natural Language Processing approach. With this application, tourists will be able to ask questions and answers to the system being built. This application uses language that is used daily for humans to communicate with each other.
Data Mining Technique in Detecting and Predicting Cyber In Marketplace Sector Waruwu, Jurisman; Hadinata, Wira; Febriyani, Siska; Wijayanti, Rini
Jurnal Informatika Vol 1 No 1 (2022): Jurnal Informatika
Publisher : LPPM Universitas Nias Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57094/ji.v1i1.350

Abstract

Marketplace is one business solution that can be profitable, because it is not bound by time and place. However, marketplace can be misused by irresponsible parties, and can harm others. Then a pattern is needed to predict cybercrime in order to prevent it. To get a pattern, we can use data mining. This paper presents a general idea about the model of Data Mining techniques and diverse cybercrimes in market place applications. This paper implements data mining techniques like K-Means, Influenced Association Classifier and J48 Prediction tree for investigating the cybercrime data sets. K-means selects the initial centroids so that the classifier can mine the record and also formulate predictions of cybercrimes with J48 algorithm. The knowledge of K-Means, Influenced Association Classifier and also J48 Prediction tree tends certainly to afford a enhanced, incorporated, and precise result over the cybercrime prediction in the marketplace sectors and prevent the cybercrime.
Implementation of Natural Language Processing On Chatbot for Tourist Information Services (Case Study: Serang City) Hadinata, Wira; Stianingsih, Lilis
INFOKUM Vol. 13 No. 02 (2025): Infokum
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v13i02.2774

Abstract

This research aims to develop and implement a chatbot system based on Natural Language Processing (NLP) that can provide tourist information services in Serang City. With the increasing need for fast and accurate information in the digital era, it is hoped that chatbots can be an effective solution to help tourists obtain information regarding tourist attractions, accommodation and activities in Serang City. The methods used in this research include data collection through interviews and surveys, as well as developing an NLP model using natural language processing techniques to understand and respond to user questions. The results of this research show that the chatbot developed is able to provide relevant and satisfying answers to users. In addition, feedback from users shows that this chatbot improves tourists' experience in exploring Serang City. It is hoped that this research can become a reference for the development of technology-based tourism information systems in other areas.
Prediction of the Impact of Bullying on Students Academic Achievement Using Linear Regression Irwan, Muhammad; Saepudin, Muhamad; Hadinata, Wira; Puspitasari, Nyi Dewi
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2745

Abstract

Student learning achievement is a crucial element in the education sector, shaped by a variety of internal and external factors. Accurately predicting student achievement remains a significant challenge for educators and researchers, especially considering the psychological impacts of bullying. This study aims to construct a predictive model using linear regression to examine the influence of bullying levels, social support, and mental health on student achievement at SMPN 4 Pasarkemis, Tangerang Regency. A review of previous studies highlights the psychological toll bullying can have on academic performance, with many focusing on predictive models or statistical methods like linear regression to quantify these impacts. The model was developed using Python on the Google Colab platform, utilizing the pandas, statsmodels, and seaborn libraries for statistical analysis and visualization of variable relationships. Employing a quantitative, associative research design, the study involved 533 student respondents. The findings reveal that social support has the strongest positive influence on academic achievement, while higher levels of bullying and poorer mental health correlate with decreased performance. Notably, among the various forms of bullying analyzed, cyberbullying emerged as having the most significant negative impact on academic achievement. Although the model explains approximately 5.5 percent of the variation in student learning achievement, the majority of influencing factors lie beyond the scope of this analysis. The model offers potential for further development into a web-based predictive information system to assist educators in the early identification of students at academic risk.
Deteksi Kepribadian dan Tumbuh Kembang Anak dengan Sidik Jari Menggunakan Algoritma K-Nearest Neighbor dan Decision Tree : Personality and Child Development Detection Using Fingerprints with K-Nearest Neighbor and Decision Tree Algorithm Gunawan, Andi; Fitriani, Chair Anggita; Hadinata, Wira; Ricesa, Wieke
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2094

Abstract

Kepribadian dan gaya belajar merupakan dua aspek penting dalam mendukung tumbuh kembang anak. Penelitian ini bertujuan untuk mengidentifikasi kepribadian dan tumbuh kembang anak berdasarkan pola sidik jari menggunakan algoritma klasifikasi K-Nearest Neighbor (K-NN) dan Decision Tree (DT). Penelitian dilakukan pada 10 partisipan di taman kanak-kanak Annisa Tangerang, dengan pengumpulan data sidik jari dari jari tengah, manis, dan kelingking menggunakan metode sidik tinta. Data kemudian dipindai dan dikategorikan ke dalam tiga pola utama: Arch, Loop, dan Whorl. Proses klasifikasi dilakukan menggunakan algoritma K-NN dan DT, serta diuji menggunakan pendekatan hold-out validation dan Leave-One-Out Cross-Validation (LOOCV). Hasil pengujian menunjukkan bahwa model klasifikasi mencapai nilai akurasi, precision, recall, dan F1 score sebesar 1.0, serta akurasi LOOCV sebesar 90%. Hasil prediksi menunjukkan bahwa 50% anak memiliki kepribadian dengan daya ingat tajam, 40% ambisius dan disiplin, serta 10% perfeksionis dan komunikatif. Gaya belajar yang teridentifikasi menggunakan K-NN adalah visual (60%) dan kinestetik (40%), sementara dengan DT terdiri dari visual (50%), kinestetik (40%), dan auditori (10%). Penelitian ini menunjukkan adanya korelasi antara pola sidik jari dengan kecenderungan kepribadian dan gaya belajar anak serta bisa menjadi alat bantu dalam deteksi dini dan intervensi edukatif berbasis karakter anak.
Co-Authors Andi Gunawan Febriyani, Siska Fitriani, Chair Anggita Hermanto, Toto Irwan Irwan Jurisman Waruwu Puspitasari, Nyi Dewi Ricesa, Wieke Saepudin, Muhamad Stianingsih, Lilis Wijayanti, Rini