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All Journal Semantik Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Elementer (Elektro dan Mesin Terapan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Akuntansi Keuangan dan Bisnis Proceeding Applied Business and Engineering Conference Proceeding International Applied Business and Engineering Conference JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Jurnal Informatika Polinema (JIP)
Kartina Diah K. W.
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Aplikasi Pengenalan Nama Surah pada Juz ke 30 Kitab Suci Al-Qur’an Menggunakan Speech Recognition Dhimas Sena Rahmantara; Kartina Diah Kesuma Wardhani; Maksum Ro’is Adin Saf
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 1 (2018): April 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (764.425 KB) | DOI: 10.29207/resti.v2i1.285

Abstract

Al-Qur’an is a scripture which contains the saying of Allah Subhanahu Wa Ta’aala and was revealed to Prophet Muhammad. The 30th juz is the juz that exists in the Al-Qur’an. When studying how to read Al-Qur’an well, the first thing that is learned is reading and memorizing surahs in the 30th juz. Nevertheless, there is a problem in remembering or knowing the surah name and the verse which are in the 30th juz. An android application was developed in order to recognize the surah names in the 30th juz by utilizing speech recognition technology to overcome that problem. Markov Model (Markov Chain) algorithm was implemented in this application. This algorithm will process user’s speech and compute probability of the surah name that was spoken. Speech detection testing gave result that the highest accuracy of application in recognizing the speeches was in the environment without noise with the accuracy of 100% in the most ideal distance is 50 cm for male and for female user. Based on the blackbox testing result, all functionalities of the application have functionated well. Control flow testing gave result that the value is 7 which indicates that the code is simple and well written. 87,74% respondents answered, by filling up the questionnaires, that the application is useful in order to make user knows better about the surah names in the 30th juz.
Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost) Kartina Diah Kusuma Wardani; Memen Akbar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4651

Abstract

Diabetes results from impaired pancreatic function as a producer of insulin and glucagon hormones, which regulate glucose levels in the blood. People with diabetes today are not only experienced adults, but pre-diabetes has been identified since the age of children and adolescents. Early prediction of diabetes can make it easier for doctors and patients to intervene as soon as possible so that the risk of complications can be reduced. One of the uses of medical data from diabetes patients is to produce a model that medical personnel can use to predict and identify diabetes in patients. Various techniques are used to provide the earliest possible prediction of diabetes based on the symptoms experienced by diabetic patients, including the use of machine learning. People can use machine learning to generate models based on historical data from diabetic patients, and predictions are made with the model. In this study, extreme gradient boosting is the machine learning technique for predicting diabetes (xgboost) using XGBoost with importance features. The diabetes dataset used in this study comes from the early stage diabetes risk prediction dataset published by UCI Machine Learning, which has 520 records and 16 attributes. The diabetes prediction model using xgboost is displayed as a tree. The model precision result in this study was 98.71%, for the F1 score was 98.18%. The accuracy obtained based on the best 10 attributes using the importance of the XGBoost feature is 98.72%.
Co-Authors Akbar, Memen Alfin H Anam, M Khairul Ardianto Wibowo Ari Azhar Cakra Ramadhana chintia romauli rambe Rambe Dhimas Sena Rahmantara Efrizoni, Lusiana Heri Subagiyo Ike Yunia Pasa Jan Alif Kreshna Juni Nurma Sari Khairul Umam Syaliman Maksum Ro’is Adin Saf Maksum Ro’is Adin Saf Mardhiah Fadhli, Mardhiah Memen Akbar Muhammad Bambang Firdaus Nurmalasari, Dini Puja Hanifah Sri Devi Anjasmara Sugeng Purwantoro E.S.G.S Surya, Ibnu Syaparudin BS Tisa Magrisa Tobi Arfan Wawan Yunanto Wenda Novayani Weslie Weslie Yohana Dewi Lulu Widyasari Yuli Fitrisia Yuliska Yuliska