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All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Danny Sebastian
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Computer Science & IT

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Rancang Bangun Website Klasikfikasi Untuk Pencarian Produk Pasar Online Berdasarkan Danny Sebastian
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 3 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i3.685

Abstract

In the past few years, online marketplace has improved so fast. There are many product listed at online marketplace. This growth make a new problem for buyers, it’s too much information. Which is too many online marketplaces and products listed online. From the above problems, came the idea for making a website that could help user to search multiple online marketplace at once, and help the user find the desired products. In this research will be generated website design that can help buyers to find products that their desired. The website will be able to do a certain classification by using classification algorithm, K-Nearest Neighbor.
Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace Danny Sebastian
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i1.1581

Abstract

E-marketplace has gained popularity with the Indonesian society resulting in the increment of products offered. Consequently, customers require more effort to search for products. In this study, we classified products from several e-marketplaces. The classification was carried out using TF-IDF method for the weighting, cosine similarity to calculate product similarity distance, and k-nearest neighbor algorithm. Based on the first testing result using 150 product data, the k-nearest neighbor method with k=5 successfully classified 146 data with 4 data classified into the wrong class. This k=5 value gives the best result for this case, with an accuracy of 97.33%. The second testing result using 150 mixed brand product data, the k-nearest neighbor method successfully classified 145 data with 5 data classified into the wrong class. The accuracy of the second testing is 96.67%.
Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering Ruddy Cahyanto; Antonius Rachmat Chrismanto; Danny Sebastian
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.3006

Abstract

Clustering is a technique in data mining thatgroups data sets into similar data clusters. One of thealgorithms that is commonly used for clustering is K-Means.However, the K-Means algorithm has several weaknesses, oneof them is the random factor in initial centroid selection, sothat cluster result is inconsistent even though it is tested withthe exact same data. The Modified K-Means algorithm focuseson selecting the initial centroid to overcome inconsistencies ofcluster results in the K-Means method. The test was conductedusing sentipol dataset and only focused on comment data.Furthermore, the specified number of clusters is 3 based on thenumber of existing comment labels (positive, negative, andneutral). According to testing result proves that Modified KMeans algorithm produces better purity value than K-Meansalgorithm. Modified K-Means algorithm produces average ofpurity value 0,42, while K-Means produces average of purityvalue 0,391. Meanwhile, from testing related to random factorsconducted 5 times with the same attributes and test data, theresults of the cluster on the Modified K-Means algorithm didnot change, so automatically the resulting purity value was alsothe same. Whereas in the K-Means algorithm, the clusterresults always change in each test, so the result of purity valueis also likely to change.
Perancangan Antarmuka Berdasarkan Evaluasi Usabilitas Penggunaan Aplikasi KlikDokter Untuk Pralansia dan Lansia Christianti Angelin Maarende; Danny Sebastian; Restyandito Restyandito
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 3 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i3.4081

Abstract

The situation and condition of the spread of Covid-19 in Indonesia have forced everyone to reduce the intensity of going out of the house, including activities related to the need for health services. Therefore, a solution is needed so that people can meet their health needs without leaving the house. Taking into account that the elderly as the community group that is most vulnerable to contracting the virus and has the highest mortality rate, it can be said that the elderly is the group of people who most need online health services. Along with the growing penetration of internet usage and the increasing number of smartphone ownership in Indonesia, m-health is the right choice to help people access health services online via smartphones. KlikDokter is an example of m-health or a mobile-based application that provides various online health services. However, it was found that there were complaints that the KlikDokter application was too complicated and difficult for the elderly to use. From this problem, a usability test was finally carried out on the KlikDokter application interface, to identify what interface elements in the KlikDokter application were difficult for elderly users. The research respondents were divided into two groups with 16 and 17 people respectively. The first group is the elderly group aged >60 years. And the second group is the pre-elderly group with ages between 45 to 59 years. A pre-elderly group is a comparison group (control group). This test is carried out by measuring several aspects of usability, namely effectiveness, efficiency, user satisfaction, error, and cognitive load.
Co-Authors Antonius Rachmat Chrismanto Christianti Angelin Maarende Restyandito Ruddy Cahyanto