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Aplikasi Diagnosa Penyakit Jantung dan Sistem Pelacakan Rumah Ssakit Menggunakan Alogritma Fuzzy-Tsukamoto dan Edsger Dijkstra Berbasis Android untuk Masyarakat Indonesia Sari, Azani Cempaka; Nindito, Hendro; Maryani, Maryani
Teknik dan Ilmu Komputer VOL. 7 NO. 25 Januari-Maret 2018
Publisher : Teknik dan Ilmu Komputer

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AbstrakAdapun penelitian ini merancang aplikasi diagnosa penyakit jantung dan sistem pelacakan rumah sakit berbasis android untuk masyarakat Indonesia. Hasil penelitian ini dapat memberikan gambaran dan penejelasan kepada user dalam mendiagnosa lebih dini penyakit jantung berdasarkan gejala-gejala atau penyebab yang ada dengan cepat dan tepat. Selain itu, aplikasi ini mempermudah user dalam memberikan gambaran mengenai informasi kesehatan penyakit jantung yang dialami dan memberikan pengetahuan tentang jenis-jenis penyakit jantung dan gejala, penyebab disertai tindakan yang harus diambil untuk pencegahannya sebagai langkah awal dalam mengantisipasi penyakit jantung. Di sisi lain, aplikasi ini juga dilengkapi fitur-fitur lainnya, seperti artikel, video, dan info rumah sakit terkait dan terdekat yang dapat dilacak oleh user.Kata Kunci: aplikasi, diagnosa, jantung, pelacakan, AndroidAbstract This study aims to design android-based heart disease diagnosis application and hospital tracking system for Indonesian community. The result of this study can provide a quick and precise overview and explanation about early diagnosis of heart disease based on the existing symptoms or causes to the users. In addition, it also provides an overview on the heart disease experienced and provides knowledge about the types of heart disease, the symptoms, the causes along with actions to take for prevention. In addition, this application also features articles, videos, and partner hospitals close to the userâs location.Keywords: Application, Diagnosis, Heart, Tracking, Android, Society, IndonesiaTanggal Terima Naskah : 07 Juni 2017Tanggal Persetujuan Naskah : 20 Oktober 2017

The Application Of K-Means Algorithm For LQ45 Index on Indonesia Stock Exchange Condrobimo, A. Raharto; Sano, Albert V. Dian; Nindito, Hendro
ComTech: Computer, Mathematics and Engineering Applications Vol 7, No 2 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i2.2256

The objective of this study is to apply cluster analysis or also known as clustering on stocks data listed in LQ45 index at Indonesia Stock Exchange. The problem is that traders need a tool to speed up decision-making process in buying, selling and holding their stocks.The method used in this cluster analysis is k-means algorithm. The data used in this study were taken from Indonesia Stock Exchange. Cluster analysis in this study took data’s characteristics such as stocks volume and value. Results of cluster analysis were presented in the form of grouping of clusters’ members visually. Therefore, this cluster analysis in this study could be used to identify more quickly and efficiently about the members of each cluster of LQ45 index. The results of such identification can be used by beginner-level investors who have started interest in stock investment to help make decision on stocks trading.

Replikasi Unidirectional pada Heterogen Database Nindito, Hendro; Madyatmadja, Evaristus Didik; Sano, Albert Verasius Dian
ComTech: Computer, Mathematics and Engineering Applications Vol 4, No 2 (2013): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v4i2.2656

The use of diverse database technology in enterprise today can not be avoided. Thus, technology is needed to generate information in real time. The purpose of this research is to discuss a database replication technology that can be applied in heterogeneous database environments. In this study we use Windows-basedMS SQL Server database to Linux-based Oracle database as the goal. The research method used is prototyping where development can be done quickly and testing of working models of the interaction process is done through repeated. From this research it is obtained that the database replication technolgy using Oracle Golden Gate can be applied in heterogeneous environments in real time as well.

Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia Sano, Albert V. Dian; Nindito, Hendro
ComTech: Computer, Mathematics and Engineering Applications Vol 7, No 2 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i2.2254

The objective of this study was to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem was that the decision makers such as central government, local government and non-government organizations, which involved in poverty problems, needed a tool to support decision-making process related to social welfare problems. The method used in the cluster analysis was kmeans algorithm. The data used in this study were drawn from Badan Pusat Statistik (BPS) or Central Bureau of Statistics on 2014.Cluster analysis in this study took characteristics of data such as absolute poverty of each province, relative number or percentage of poverty of each province, and the level of depth index poverty of each province in Indonesia. Results of cluster analysis in this study are presented in the form of grouping ofclusters' members visually. Cluster analysis in the study can be used to identify more quickly and efficiently on poverty chart of all provinces all over Indonesia. The results of such identification can be used by policy makers who have interests of eradicating the problems associated with poverty and welfare distribution in Indonesia, ranging from government organizations, non-governmental organizations, and also private organizations.

Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia Albert V. Dian Sano; Hendro Nindito
ComTech: Computer, Mathematics and Engineering Applications Vol. 7 No. 2 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i2.2254

The objective of this study was to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem was that the decision makers such as central government, local government and non-government organizations, which involved in poverty problems, needed a tool to support decision-making process related to social welfare problems. The method used in the cluster analysis was kmeans algorithm. The data used in this study were drawn from Badan Pusat Statistik (BPS) or Central Bureau of Statistics on 2014.Cluster analysis in this study took characteristics of data such as absolute poverty of each province, relative number or percentage of poverty of each province, and the level of depth index poverty of each province in Indonesia. Results of cluster analysis in this study are presented in the form of grouping ofclusters' members visually. Cluster analysis in the study can be used to identify more quickly and efficiently on poverty chart of all provinces all over Indonesia. The results of such identification can be used by policy makers who have interests of eradicating the problems associated with poverty and welfare distribution in Indonesia, ranging from government organizations, non-governmental organizations, and also private organizations.

The Application Of K-Means Algorithm For LQ45 Index on Indonesia Stock Exchange A. Raharto Condrobimo; Albert V. Dian Sano; Hendro Nindito
ComTech: Computer, Mathematics and Engineering Applications Vol. 7 No. 2 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i2.2256

The objective of this study is to apply cluster analysis or also known as clustering on stocks data listed in LQ45 index at Indonesia Stock Exchange. The problem is that traders need a tool to speed up decision-making process in buying, selling and holding their stocks.The method used in this cluster analysis is k-means algorithm. The data used in this study were taken from Indonesia Stock Exchange. Cluster analysis in this study took data’s characteristics such as stocks volume and value. Results of cluster analysis were presented in the form of grouping of clusters’ members visually. Therefore, this cluster analysis in this study could be used to identify more quickly and efficiently about the members of each cluster of LQ45 index. The results of such identification can be used by beginner-level investors who have started interest in stock investment to help make decision on stocks trading.

Replikasi Unidirectional pada Heterogen Database Hendro Nindito; Evaristus Didik Madyatmadja; Albert Verasius Dian Sano
ComTech: Computer, Mathematics and Engineering Applications Vol. 4 No. 2 (2013): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v4i2.2656

The use of diverse database technology in enterprise today can not be avoided. Thus, technology is needed to generate information in real time. The purpose of this research is to discuss a database replication technology that can be applied in heterogeneous database environments. In this study we use Windows-basedMS SQL Server database to Linux-based Oracle database as the goal. The research method used is prototyping where development can be done quickly and testing of working models of the interaction process is done through repeated. From this research it is obtained that the database replication technolgy using Oracle Golden Gate can be applied in heterogeneous environments in real time as well.

Evaluation of Social Media in Digital Supply Chain Management Didik Madyatmadja, Evaristus; Nindito, Hendro; Pristinella, Debri; Tannady, Hendy
International Journal of Supply Chain Management Vol 9, No 5 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v9i5.5499

In the past few years, many economies implement a social media-based digital supply chain management application for improving performance. However, there is problem regrading e-participation because of low level of participation by the public. The solution for improving public e-participation is by make a media for public do the e-participation, the media is social media-based digital supply chain management application. In fact, when a economy already implement the application, the participation is low. The purpose of this research is to identify factors that affecting behavior intention of public for do the e-participation using social media-based digital supply chain management application. In this research there are 203 social media-based digital supply chain management application users in Special Capital Region of Jakarta (Jakarta Aman application) that becoming research subject for evaluating the application for future development or references for other economy. The methodology approach for this study is based on combining many models such as Delon and McLean’s IS Success Model and TPB as the main model. Also combining external factors from existing studies that affecting attitude toward using the application. This paper can become references and contributing for making reader to understand more regrading people behavior for adopting social media-based digital supply chain management application to engage in e-participation. Social media can be an invaluable tool for supply chain professionals looking to identify new innovations, understand commodity and pricing trends, capture best practices, and collaborate with stakeholders, peers, and suppliers.

MENGOPTIMALKAN ORACLE SPASIAL UNTUK ANALISIS KEDEKATAN GEOGRAFIS Nindito, Hendro
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.318

The study explores the utilization of Oracle Spatial in the determination of the shortest path between locations. This is very important for the analysis and management of geographic information. Its ability to handle multiple attributes, rasters, and vectors, is greatly enhanced by its support for data management. The objective of the study is to find the closest locations of Binus BB S and Binus Square to investigate the use of Oracle's Geospatial technology. The research entails populating a table with geometry clusters, performing spatial queries, and capturing data. The study utilized SDO_GEOMETERY and SDO_NN to find the Binus Square and BBS campuses' closest locations. The findings reinforced Oracle Spatial's practicality and accuracy for proximity analysis, emphasizing the significance of maintaining and managing such databases. The study also identified an issue which suggests that the product could be improved in the future. The integration of its features with other Oracle applications can provide more effective management and visualization of spatial data. The study highlighted Oracle Spatial's potential to support complex spatial analyses and improve the operations of spatial databases. ABSTRAKPenelitian ini mengeksplorasi pemanfaatan Oracle Spatial dalam penentuan jalur terpendek antar lokasi. Hal ini sangat penting untuk analisis dan pengelolaan informasi geografis. Kemampuannya untuk menangani banyak atribut, raster, dan vektor, sangat ditingkatkan dengan dukungannya terhadap manajemen data. Tujuan penelitian adalah mencari lokasi terdekat Binus BBS dan Binus Square untuk mengetahui penggunaan teknologi Geospasial Oracle. Penelitian ini memerlukan pengisian tabel dengan cluster geometri, melakukan kueri spasial, dan menangkap data. Penelitian ini memanfaatkan SDO_GEOMETERY dan SDO_NN untuk mencari lokasi terdekat kampus Binus Square dan BBS. Temuan ini memperkuat kepraktisan dan akurasi Oracle Spatial untuk analisis kedekatan, menekankan pentingnya memelihara dan mengelola database tersebut. Studi ini juga mengidentifikasi masalah yang menunjukkan bahwa produk tersebut dapat ditingkatkan di masa depan. Integrasi fitur-fiturnya dengan aplikasi Oracle lainnya dapat memberikan pengelolaan dan visualisasi data spasial yang lebih efektif. Studi ini menyoroti potensi Oracle Spatial untuk mendukung analisis spasial yang kompleks dan meningkatkan pengoperasian database spasial

STUDI PERBANDINGAN KEAKURATAN MODEL GLM DAN SVM DALAM MEMPREDIKSI TINGKAT PENGANGGURAN DI INDONESIA Nindito, Hendro; Imanuel, Marchelle; Calvin, Calvin; Lukman, Michelle Pandojo
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.374

Unemployment is one of the major issues faced by Indonesia. As of February 2024, the open unemployment rate in Indonesia reached 4.82% of the total labor force. The decline in labor force participation rates and the Human Development Index (HDI) in provinces with high open unemployment rates indicates a correlation as a key contributing factor to unemployment. This study aims to predict the open unemployment rate in regions of Indonesia using the Generalized Linear Model and Support Vector Machine algorithms through Oracle Machine Learning, and to compare the accuracy of both models in predicting regional unemployment levels in Indonesia. The CRISP-DM framework was applied to support a structured analytical process. The results of the study show that the Generalized Linear Model developed to predict the open unemployment rate in Indonesia achieved a Mean Absolute Error (MAE) of 0.156 and a Root Mean Square Error (RMSE) of 0.246. In comparison, the Support Vector Machine model yielded a lower MAE of 0.014 and an RMSE of 0.097.

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