Objective: This study aimed to screen for hypertension in a vast Indonesian population using machine learning (ML) and 11 non-laboratory risk factors, validating the results through internal and external validations.
Setting and participants: From the initial 1 782 365 participants aged 15 and above registered at the Integrated Counseling Post primary care centres across Indonesia from 2014 to 2017, incomplete data and outliers were excluded, and 268 210 participants were included in our analysis. The dataset was split deterministically into a dataset for training using 10-fold internal cross-validation of 204 315 participants and another dataset for external validation of 63 895 participants.
Design: This retrospective cross-sectional study used three ML algorithms, that is, random forest, gradient boosting and extreme gradient boosting (XGBoost), and compared them against logistic regression as a benchmark to screen hypertension based on the WHO and International Society of Hypertension criteria. The importance of the risk factors was ranked. By partly using continuous versus categorical age, waist circumference (WC) and body mass index (BMI) risk factors, we evaluated the screening performance regarding sensitivity and area under the receiver operating characteristic curve (AUC).
Results: The external validations revealed that the XGBoost model performed the best in hypertension screening. The external validation, which partly uses continuous variables, provides 0.97 sensitivity and 0.75 AUC, indicating excellent screening capability. The importance rank of the risk factors was consecutively family history of hypertension (FH-HTN), age, WC, BMI, occupation, education, sex, smoking, low physical activity, lack of fruit or vegetable intake and alcohol consumption.
Conclusions: By using 11 easy-to-collect non-laboratory risk factors, the ML model successfully screens for hypertension with better performance than the benchmark. Using the numerical variables of age, WC and BMI yields a better discrimination capability than the categorical variables. FH-HTN and age are the two top risk factors for the development of hypertension. This study is a useful academic exercise and shows ML’s importance in handling large data sets.
Indonesia and Malaysia from 2020 to 2021 were exposed to COVID-19 pandemic. Both countries implemented a policy of restricting entry areas based on almost the same criteria, In Indonesia namely as PPKM which applying some level of exposure to those infected with covid-19. The determination of this level was all based on the growth in numbers exposed to covid-19, but on pandemic cases, the number of people who do not suffer from COVID-19 disease but have the same symptoms as the symptoms of COVID-19 also need to be considered as the pandemic agent to their environment. We named it as Precaution Covid-19 Pandemic (PCP) Level. The current level of the COVID-19 pandemic has not been fully determined by this idea. So, the idea of this research is to determine the pre-pandemic or precaution level of covid-19 in an area interfere by surrounding area. PCP level was not based on the growth of those infected with the covid-19 disease, but influenced by the number of patients whose have the symptoms similar to the dominant symptoms of the covid-19. The PCP Level determination can be used for precaution policy and support the previous Level Pandemic Methods. To accomplish this idea, three algorithms are used, they are K-Mean algorithm as a pattern clustering and the AHP algorithm as a level determination of the Covid-19 pandemic, While the relationship of candidate symptom pairs to Covid-19 transmission is carried out using the Naïve Bayes algorithm. The results of this study show that the combination of the three proposed algorithms provides and using data symptoms closely to dominant covid-19 symptoms can give an alternative for precaution level of covid-19 pandemic. The model for determining Covid-19 transmission based on four candidate symptoms has 89% precision and 85% accuracy. Published in 2022 Seventh International Conference on Informatics and Computing (ICIC)
Adaptation of new technology in e-learning begins with psychological motivation from students, where this becomes important related to the success of the e-learning concept. Examining the phenomenon, this study aims to investigate the relationship between student psychological motivation and expected benefits, e-learning curriculum, and educational partners. The research was conducted on students in the city of Bandung through a quantitative survey. The data from students was taken through a questionnaire and the tabulated data from the questionnaire was processed using SmartPLS. There is a research model based on the design of the research hypothesis being tested. The results showed that the expected benefit and e-learning curriculum had a positive relationship with psychological motivation. Meanwhile, educational partners do not have a positive relationship with psychological motivation. Furthermore, it is known that the expected benefit has the biggest impact in supporting the psychological motivation of students. Psychological motivation is important related to the adaptation of new technology in e-learning, because it is a factor that can increase the success of using e-learning as a learning medium. The findings of this study can be used as input for universities in reviewing the concept of e-learning so that it can be well received by its users.
Indonesia has been listed among big twenty countries with highest fossil fuel energy consumption. Indonesians are highly dependent on the sustainability of this fuel supply. Public Gas Station and PT Pertamina (Persero) in Indonesia play a very critical role in domestic fuel supply chain. According to the analysis of Badan Pengatur Hilir Migas, these parties are still facing one big issue, i.e maintaining the sustainability of fuel oil supply to all over the country. Meanwhile, the current integrated supply chain only fully covers the upstream sector. It leaves an open problem to be cleared. One important thing in maintaining the sustainability of supply chain is inventory control. Good inventory control will keep the sustainability of fuel supply to the citizens which become the value in public gas station service. A smart inventory control applied technology as its component. As cyber physical social system (CPSS) technology has been known having the capability of solving everyday life problems by the activities automation and reliable information flow, it is worth proposing a CPSS implementation in inventory control system as a service innovation of the public gas station in order to solve the problem and fulfill the goal of excellent service and customer satisfaction.
BANDUNG, UNIKOM — The spirit of achievement has once again been strengthened within the Universitas Komputer Indonesia (UNIKOM) environment through the organizing of the 2026 University-Level Outstanding Student Selection (PILMAPRES) on Tuesday, April 7, 2026. Taking place in Room L.021 of the UNIKOM Smart Building, the event served as a strategic platform to identify and develop the excellent potential of students, in both academic and non-academic fields.
The University-Level Outstanding Student Selection (PILMAPRES) involved a panel of five judges from various academic disciplines within UNIKOM, namely the Vice Rector I for Academic and Student Affairs, Prof. Dr. Hj. Umi Narimawati, Dra. S.E., M.Si.; UNIKOM Professor, Prof. Dr. Hj. Aelina Surya, Dra.; Dean of the Faculty of Design, Prof. Dr. Ir. Lia Warlina, M.Si.; Director of Student Affairs, Andrias Darmayadi, S.IP., M.Si., Ph.D.; and the Head of the UNIKOM Language Center Division, Dr. Retno Purwani Sari, S.S., M.Hum.
The participants of the University-Level Outstanding Student Selection (PILMAPRES) were the best student representatives from all faculties at UNIKOM who had undergone a tiered selection process, starting from the study program level up to the faculty level. At this university level, the students who achieved the first rank for the Undergraduate (S1) and Diploma (D3) programs will respectively represent UNIKOM at the Outstanding Student Selection (PILMAPRES) at the Higher Education Service Institution (LLDIKTI) Region IV, West Java and Banten Province level.
The Vice Rector for Academic and Student Affairs of UNIKOM, Prof. Dr. Hj. Umi Narimawati, Dra., S.E., M.Si., in her opening remarks expressed her appreciation for the successful implementation of the event. Prof. Umi emphasized that the Outstanding Student Selection (PILMAPRES) is an essential medium for exploring the potential and achievements of UNIKOM students. “Thank you to the Directorate of Student Affairs for organizing this university-level Outstanding Student Selection (PILMAPRES). This event serves as a strategic space to measure and develop students’ abilities, both academically and non-academically. Based on my experience as a PILMAPRES judge at the Higher Education Service Institution (LLDIKTI) Region IV level, one of UNIKOM’s challenges lies in the Excellent Achievements (Capaian Unggulan) aspect, which still needs to be improved. Therefore, a collective commitment is required to continuously encourage students to actively compete up to the national and international levels. This is critical considering the substantial weight of the Excellent Achievements assessment in the University-Level Outstanding Student Selection (PILMAPRES) selection. The students selected as champions will continue to be mentored to maximize preparations for the next level,” stated Prof. Umi.
The series of events continued with the assessment process, which was carried out comprehensively in two separate rooms. The Excellent Achievements (Capaian Unggulan) Assessment was held in Room L.030 by Prof. Dr. Hj. Umi Narimawati, M.Si., M.Pd., Prof. Dr. Hj. Aelina Surya, Dra., and Andrias Darmayadi, S.IP., M.Si., Ph.D. Meanwhile, the assessment of the Creative Idea Manuscript (S1) and the Innovative Product Manuscript (D3) took place in Room L.021 by Prof. Dr. Ir. Lia Warlina, M.Si., which was followed by an English interview session by Dr. Retno Purwani Sari, S.S., M.Hum.
Based on the strict and objective assessment results, the jury determined the ranking of the 2026 University-Level Outstanding Students for the Undergraduate (S1) Program as follows:
1st Place: Febby Cipta (NIM 44324048) – International Relations Study Program.
2nd Place: Muhammad Faisal Hafiz (NIM 51923008) – Visual Communication Design Study Program.
3rd Place: Ghaitsa Amalua (NIM 41823153) – Communication Science Study Program.
4th Place: Alisya Yansa (NIM 30623004) – Law Study Program.
5th Place: Vanessa Rainaya Disam (NIM 31623006) – Law Study Program.
6th Place: Reynaldhy Putra Herdiansyah (NIM 21223159) – Management Study Program.
7th Place: Echfiz Dzakwan Arrazie (NIM 63724013) – English Literature Study Program.
8th Place: Dulce Clarissa Juliety Latuputty (NIM 10623001) – Urban and Regional Planning Study Program.
As for the Diploma (D3) Program, the first place was secured by Mursyida Sakina (NIM 21424008) from the Marketing Management Study Program.
The Director of Student Affairs of UNIKOM, Andrias Darmayadi, S.IP., M.Si., Ph.D., also expressed his appreciation for the successful execution of the event. “Alhamdulillah, the implementation of the 2026 University-Level Outstanding Student Selection has run well and smoothly. I congratulate the winners, both from the Undergraduate and Diploma programs, who will represent UNIKOM at the Higher Education Service Institution (LLDIKTI) level. Hopefully, the delegates will be able to demonstrate their best performance and compete up to the national level. We also appreciate all participants who have shown excellent academic quality, and we express our gratitude to the judges and the committee for their dedication and hard work,” he concluded.
Through this Outstanding Student Selection (PILMAPRES) event, UNIKOM continues to assert its commitment to producing a superior generation that is competitive, innovative, and ready to contribute at both national and international levels. (Directorate of Hms & Pro)
