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All Journal Proceeding International Conference on Information Technology and Business Journal of Applied Data Sciences
Nurjoko Nurjoko
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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THE DEVELOPMENT OF E-CRM FOR THE SERVICE IMPROVEMENT AT THE UNIVERSITIES Sriyanto Sriyanto; Sri Karnila; Nurjoko Nurjoko
Prosiding International conference on Information Technology and Business (ICITB) 2016: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 2
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

Public Universitiesand Private Universities are recently contesting each other to improve their qualities and facilities in order to attract new students to come in. The high quality and the number of facilities belonging to universities are not a guarantee to get prospective students as they are targeting. This phenomenon occurs due to many factors. One of the factors causing the matter is that the way in delivering information to the prospective students and their parents is not right and not compatible with their needs so that the prospective students and their parents do not know the quality, the educational programs, the facilities, and another information which belong to the universities. It also occurs in IBI Darmajaya, one of the top private universities in Lampung. According to the obtaineddata of this research, the number of the new students who joinedgenerally decline compared with the previous year; while, the number of students who did not finish theirstudy was high enough. One of the solutions to overcome these problems was by providing information through the exact media which provided the needs of the prospective students and their parents. By using this way, it was expected to convince the prospective students to join and to finish their study.In order to provide the information which was compatible with the needs of the prospective students and their parents, the E-CRM system equipped by the data mining was developed. The database which was provided in IBI Darmajaya was processed by the data mining in order to be used as an input for the E-CRM system. This system providedthe information for the relevant unit about what the informational media which had to be used were and what information which should be given to the prospective students and their parents was. To find out the successfulness of the implementation of this system, the Critical Success Factor (CSF) was carried out. The result of the CSF was used for the improvement of the system and the expansion of the system of the E-CRM.Key Words:E-CRM, Zachman, CSFTHE DEVELOPMENT OF E-CRM FOR THE SERVICE IMPROVEMENT AT THE UNIVERSITIES
Internet of Things (IoT) Application in Smart Farming to Optimize Tomato Growth Dodi Yudo Setyawan; Rahmalia Syahputri; Nurfiana Nurfiana; Nurjoko Nurjoko
Prosiding International conference on Information Technology and Business (ICITB) 2022: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 8
Publisher : Proceeding International Conference on Information Technology and Business

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Food is a vital need in human life and has become a significant commodity. In this sector, FAO Food and Agriculture Organization predicted in 2050 it will meet the food needs of 9.6 billion people. In addition, the reduction of agricultural land and climate change also contribute to the list of challenges in this sector. These challenges may trigger, for instance, a decline in the volume of agriculture yield, an increase in disease, and shifting cultivation periods. Therefore, it is a concern for all countries whose population growth rates keep increasing, including Indonesia. The fundamental thing in the Indonesian agricultural sector is the need for mass production of agricultural products to improve food security for its 275 million people. We can take advantage of cutting-edge Information and Communication Technology in the form of IoT to manage agricultural land and equip farmers in planning, monitoring, and managing agricultural land.   This research aimed to compare the production of tomatoes that plant integrated by the IoT system with convention in a greenhouse situated in Cibodas, Lembang, West Java. The IoT system applied to smart farming uses the Message Queuing Telemetry Transport (MQTT) protocol. We found the productivity of integrated tomatoes increased by about 19% compared to conventional plants. Moreover, the Grade A-B of tomatoes increased by 9,8% while off-grade decreased by 9,6 %.  Thus, IoT can optimize the yield of limited plant media.Keyword—Food; Agriculture; IoT; Greenhouse; Tomatoes
Performance Evaluation of Support Vector Machine (SVM) and XGBoost for Predicting Toddlers’ Stunting Status Based on Anthropometric Data Nurjoko Nurjoko; Admi Syarif; Favorisen R. Lumbanraja; Khairunisa Berawi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1260

Abstract

Stunting remains a primary global health concern, particularly in developing countries, due to its long-term effects on physical growth, cognitive development, and overall well-being. Despite various public health initiatives, challenges in early detection persist, highlighting the need for accurate, data-driven predictive models to support targeted interventions. This study aims to develop and compare the performance of two machine learning algorithms—SVM and Extreme Gradient Boosting (XGBoost)—for classifying stunting status among children under five, in order to determine the most effective method for early prediction. A quantitative machine learning approach was applied to a dataset comprising 17,498 records derived from Posyandu data in Lampung Province, Indonesia. The analytical pipeline included data preprocessing, class rebalancing using the Synthetic Minority Over-sampling Technique (SMOTE), and model evaluation through stratified 10-fold cross-validation. Performance was assessed using accuracy, precision, recall, and F1-score. The XGBoost model demonstrated superior performance with accuracy, precision, recall, and F1-score reaching 0.9979. In comparison, the SVM model produced slightly lower yet still strong results, achieving an accuracy of 0.9949, with similarly consistent performance across other evaluation metrics. These findings indicate that XGBoost more effectively handles high-dimensional, imbalanced data and captures nonlinear patterns in the dataset. XGBoost was identified as the optimal method for stunting classification in this study, outperforming SVM across all evaluation metrics. These results support the integration of boosting-based models into early detection systems for child nutritional assessment. Future studies should incorporate additional environmental and socioeconomic variables and evaluate model applicability in a real-time community health setting.
Co-Authors Abda Abda Admi Syarif Khairun Nisa Lumbanraja, Favorisen R Nurfiana Nurfiana Rahmalia Syahputri Sri Karnila