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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH JSI (Jurnal sistem Informasi) Universitas Suryadarma International Journal of Educational Research and Social Sciences (IJERSC)
Aji Nurrohman
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Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Decision Support System For The Best Student Selection Recommendation Using Ahp (Analytic Hierarchy Process) Method Dona Katarina; Aji Nurrohman; Wibisono; Priatno; Arman Syah Putra
International Journal of Educational Research & Social Sciences Vol. 2 No. 5 (2021): October 2021
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijersc.v2i5.186

Abstract

The background of this research is how to choose the best students in school using the AHP algorithm. Then it can be determined who the best students are. Based on the system that will be input and processed by the system so that it will produce maximum output and not in favor of anyone. The method used in this study is to use the literature study method. And literature study using these 2 methods will be able to provide something new, especially in the field of problem formulation and novel poles will be found after that the method is to conduct trials by inputting process and output so that you can find out who the best students in school. The problem raised in this study is how to determine the best students. Based on the system not based on the choice of someone with the system, it will help the school in determining the best students so that it does not side with one party and choose the wrong choice. In this study will produce data that can be known which students will be selected to be the best students in school based on the process of the AHP algorithm that has been included in this study.
ANALISIS DAN RANCANG BANGUN PROTOTYPE KANOPI PELINDUNG JEMURAN BERBASIS INTERNET OF THING (IOT) Aji Nurrohman; Berliyanto .; Sigit Wibisono; Surya Darma; Wibisono .; Leni Devera Asrar; Triyono Budi Santoso
JSI (Jurnal Sistem Informasi) Universitas Suryadarma Vol. 11 No. 1 (2024): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Fakultas Ilmu Komputer dan Desain - Unsurya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v11i1.1135

Abstract

Perancangan Prototype Kanopi Pelindung Jemuran Berbasis Internet Of thing dirancang untuk memudahkan masyarakat dalam proses penjemuran pakaian atau makanan secara efektif. Saat ini banyak masyarakat yang mendapatkan masalah pada proses penjemuran, hujan yang turun secara tiba-tiba membuat masyarakat kesulitan dalam proses pengangkatan jemuran. Oleh sebab itu dirancang prototype kanopi pelindung jemuran berbasis Internet of thing yang terintergrasi dengan aplikasi blynk dengan memanfaatkan tiga sensor yang dipasang yang berfungsi untuk memonitoring dan mendapatkan data cuaca. Yaitu : sensor hujan berfungsi untuk mendeteksi turunnya hujan, sensor cahaya berfungsi untuk mendeteksi gelap atau cerahnya cahaya dan sensor suhu yang berfungsi untuk mendeteksi perubahan suhu. berdasarkan kesimpulan data yang didapatkan dari ketiga sensor tersebut, pengguna dapat memutuskan untuk menutup atau membuka kanopi yang digerakkan menggunakan motor servo. Sehingga proses penjemuran dapat dilakukan secara efektif.. Kata kunci:  Internet of thing, Blynk, Kanopi, Monitoring cuaca, Jemuran.
ANALISIS DAN VISUALISASI POLA PARAMETER CUACA HARIAN DAN BULANAN JAKARTA TIMUR DENGAN K-MEANS CLUSTERING Lola; Aji Nurrohman; Sigit Wibisono; Rachmat Setiabudi; Atang Supena
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 5 (2026): October (2026)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i5.7273

Abstract

Weather observation data produced by the Meteorology, Climatology and Geophysics Agency (BMKG) has multivariate characteristics, so it requires an analysis method that is able to identify patterns based on similarities in data characteristics. This research aims to analyze and visualize daily and monthly weather parameter patterns in the East Jakarta area using the K-Means Clustering algorithm. The dataset used is daily observation data from the Halim Perdanakusuma Meteorological Station for the period January–December 2025, totaling 365 observations with six meteorological parameters, namely TN, TX, TAVG, RH_AVG, RR, and FF_AVG. The research stages include merging data, handling missing values ​​using median imputation, data normalization using StandardScaler, data exploration, determining the optimal number of clusters using the Elbow Method and Silhouette Score, followed by the clustering process using K-Means. The evaluation results show that there are three optimal clusters (K=3), which represent variations in the daily weather characteristics of East Jakarta. The clustering results identified different characteristics, namely Cluster 0 (Hot Dry Weather), Cluster 1 (Cool Cloudy Weather), and Cluster 2 (Heavy Rainy Weather). Visualization in the form of correlation heatmaps, time series graphs, scatter plots, box plots and bar charts provide informative representations of weather parameter distribution patterns, making it easier to interpret analysis results. This research proves that K-Means Clustering is used as an effective exploratory method for identifying weather parameter patterns and visualization of meteorological analysis.
Co-Authors Arman Syah Putra Atang Supena Berliyanto Berliyanto . Dona Katarina Leni Devera Asrar Lola Priatno, Priatno Rachmat Setiabudi Sigit Wibisono Sigit Wibisono Surya Darma Surya Darma Triyono Budi Santoso Triyono Budi Santoso Wibisono Wibisono .