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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika JAM : Jurnal Aplikasi Manajemen Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal ScientiCO : Computer Science and Informatics Journal IT (INFORMATIC TECHNIQUE) JOURNAL Advance Sustainable Science, Engineering and Technology (ASSET) DedikasiMU: Journal of Community Service PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) The Indonesian Journal of Computer Science Tadulako Science and Technology Journal
Hajra Rasmita Ngemba
Information System Department, Faculty Of Engineering, Tadulako University, Jl. Soekarno Hatta No KM 9 Palu 94148, Central Sulawesi, Indonesia
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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia Pratama, Moh. Asry Eka; Hendra, Syaiful; Ngemba, Hajra Rasmita; Nur, Rosmala; Azhar, Ryfial; Laila, Rahmah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2097

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
Aplikasi Antrian Pasien Pada Dokter Praktek Umum Menggunakan Metode FIFO (First In First Out) Berbasis Android Hardianti, Hardianti; Hendra, Syaiful; Kasim, Anita Ahmad; Azhar, Ryfial; Angreni, Dwi Shinta; Ngemba, Hajra Rasmita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1478

Abstract

Currently, there are so many services in Indonesia. One of the services in the health sector is the practice of general practitioners. Services that occur at the practice of general practitioners, namely dr. Zaki Mubarak and dr. Subhan Habibi, located in Palu, often has complaints because it is still ineffective where getting these services is still done manually by means of patients coming in person and taking a queue based on the order of seats then one by one they will be served. This causes patient discomfort in waiting. To make it easier for patients who want to seek treatment, a system is needed, with this; an Android-based patient queuing application for general practice doctors was made. The application of the method used in building the system is the FIFO queuing method where patients who register earlier get medical services first. Then the average waiting time is calculated where the results obtained will be used as an estimate of the waiting time for the next patient. The application development method in this research used the prototype method and application testing uses the black box testing method. The results of this research are the application of patient queues for general practice doctors based on Android which is built to be able to take queues anywhere and anytime and obtain some information including doctor’s practice schedules, queue numbers, running queues, and estimated waiting times so that patients can estimate arrival time without having to wait long. Based on system testing with black box, the results show that the functional system is running well. Based on the average waiting time calculation, from the 60 queue data tested, the result is that the distance between queue 1 and the order is around 5 minutes.
Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia Pratama, Moh. Asry Eka; Hendra, Syaiful; Ngemba, Hajra Rasmita; Nur, Rosmala; Azhar, Ryfial; Laila, Rahmah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2097

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.
Co-Authors - Ifandi A Djufri, Isdar Abd. Rahman Sholeh Abdillah Sani, Ilham Abdul Mahatir Najar Aimar Anand Alamsyah Alamsyah - Alamsyah Alamsyah Alda Nur Pradinda Amalia, Liza Angraeni, Dwi Shinta Anita Ahmad Kasim Anjany, Ni Luh Verha Anugrah Aidin Yotolembah Anwar, Asriani Azizah Azizah Chandra, Ferri Rama Deny Wiria Nugraha Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dodu , Albertch Yordanus Erwin Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Elyana Aulia Chandra Fahlevi, Mohammad Fazrin Fathul Wahid Habibu, Rahmawati Hamid, Odai Amer Hardianti Hardianti, Hardianti I Gusti Ngurah Agung Kade Dwi Arsana Iman Setiawan Indrajaya, Muhammad Aristo Junaidi Junaidi Junus Widjaja Kade Dwi Arsana, I Gusti Ngurah Agung Laila, Rahma Lakatjinda, Adiatma Lamadjido, Moh. Raihan Dirga Putra Lapatta, Nouval Trezand Lintine, Gabriella Bamba Ratih Liza Amalia Mohamad Irfan, Mohamad Mohammad Fajri Mohammad Yazdi Pusadan Muh Alif Alghifari Muh. Aristo Indrajaya Muhamad Aristo Indrajaya Muhammad Jindan Muhammad Nauval Daffa Ulhaq Muhammad Yazdi Nouval Trezandy Lapatta Nur Muliyana Nurpati Nurpati Nursalim P. Dominggo, Nenita Penidas Fodinggo Tanaem Pradinda, Alda Nur Pratama, Moh. Asry Eka Putra Ramadhan, Adjie Putri Febrina, Annisa Rahmah Laila Raivandy, I Made Randhy Rifai, Muhammad Fajar Rina Marlina Risaldi Pata’Dungan, Adi Rizana Fauzi, Rizana Rizka Ardiansyah Rosmala Nur Ryfial Azhar, Ryfial Setijadi, Eko Sitti Ainul Yakin Smith, Jennifer Suryadi Hadi Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Ulhaq, Muhammad Naufal Daffa Uswary, Jonathan Albert Vivi Peggie Rantung Winarta, Ardhiansyah Wirdayanti Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori Zaharuddin