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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems

Janson Hendryli

Unknown Affiliation

Author-ID : 3257252

Computer Science & IT Mathematics Other

Published : 37 Documents Claim Missing Document

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PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA DECISION TREE C4.5 DENGAN TEKNIK PRUNING Isa Iskandar; Lely Hiryanto; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 1 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

The system created are used to predict the length of study period required for students to complete their studies based on their grades. The system created also have online consultation features that students use with their academic lecturer for academic consultations. To find the model tree with good accuracy, the system will use k-fold cross validation in the process of making model tree. Testing prediction system using students data from 2008 to 2012 who have completed their studies. The value data used is all mandatory courses in the Faculty of Information Technology except for thesis courses. Based on the tests performed, the system can already run and used in accordance with the design made. The test is to compare the accuracy of the selected tree model from different k values in the k-fold cross validation process. The results obtained show that if the value of k the greater, then the accuracy obtained better.

SISTEM PEMESANAN HOTEL BERBASIS WEB MENGGUNAKAN METODE APRIORI DAN SIMPLE ADDITIVE WEIGHTING Christopher Louis Fabian; Bagus Mulyawan; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Web-based hotel application using Apriori method, the use of method based on the number of transactions that users do. Then look for the value of each itemset combination, which will then eliminated by the predetermined support value, after obtaining the result of elimination, then the association rule will be found to obtain a confidence value that will result in a percentage of each combination of facilities that have been eliminated.For the use of Simple Additive Weighting method, the data taken based on the total existing hotel, there are 250 hotels obtained from traveloka website. First, to find the SAW value determines the initial matrix to record the name of the hotel along with the given criteria, after which search fot the normalization matrix of the smallest and largest value divisions in each criteria, and the last step is the calculation of the vector weights can be determined by each user to determine priority criteria. After getting the result of the value of each hotel it will be sorted form the biggest point, so it will show the list of hotels with the biggest SAW points.

Pembuatan Website Online Store Dilengkapi dengan Chatbot Fredickson Dinata; Viny Christanti Mawardi; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

The advance of technology and the increased number of internet usage, have caused many companies to build their online store as a way to market their product. But getting the attention of people isn’t an easy thing to achieve, so livechat and chatbot are implemented into the system to increase the quality of services. This chatbot was developed using the Vector Space Model which will calculate the similarity of each question and the input, before using the vector space model each question will be weighted with term weighting. The chatbot was tested directly and the result is calculated to get the precision of 0.813, recall of 0.751, and f-measurement of 0.766. From the results, we can say the performance of the chatbot is quite decent for it has increased the quality of the services which the online store provided.

PENGEMBANGAN SISTEM AGREGATOR BERITA BAHASA INDONESIA MENGGUNAKAN CONTENT EXTRACTION DAN HIERARCHICAL AGGLOMERATIVE CLUSTERING Stenly Tirta Wijaya; Viny Christanti Mawardi; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 4, No 2 (2016): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

The main focus of this study is to develop system to aggregate Indonesian online newspaper and cluster it according to its topic automatically. The system use content extraction to get the main content of articles and Hierarchical Agglomerative Clustering to group articles by its topic with Dice Similarity Coefficient for similarity measure. To determine the cutting point, we cut dendrogram where the gap between two successive combination similarities is largest. Additionally, we add threshold to limit cutting area to improve cluster result. We use Standard Boolean Model for searching feature and Silhouette to evaluate cluster results. Test results using 998 articles shows that limiting cutting area with 0.1 and 0.5 can produce highest average silhouette value 0.264.

PENCARIAN OBJEK WISATA BERSEJARAH DI PULAU JAWA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Nadia Ramadhani; Janson Hendryli; Dyah Erny Herwindiati
Jurnal Ilmu Komputer dan Sistem Informasi Vol 7, No 1 (2019): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

In order to make a image-based search for historical sites in Java, Convolutional Neural Network is used. Not only to make a website which can search historycal sites in Java using image, it also compared two CNN structure. There are two architechture which is used in this paper which is Residual Network and Inception. There are some experiments that were done to establish the best architecture among the two for this aplication. Those experiments showed that inception gave a better result for the application

SISTEM ANALISIS KINERJA SALES BERDASARKAN TRANSAKSI PENJUALAN DENGAN REGRESI LINEAR DAN ALGORITMA APRIORI Tania Rizgitta; viny Christanti Mawardi; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 7, No 2 (2019): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Sebuah perusahaan mempunyai sales untuk menawarkan produk atau jasa yang dijual. Sales ini dianggap sebagai representative dari perusahaan dimana sales inilah yang memasarkan dan menjual produk perusahaan kepada customer. Pentingnya nilai-nilai yang dipegang oleh seorang sales sebagai sumber pendapatan bagi perusahaan. Maka dari itu, menganalisis penjualan tiap sales, dan mengetahui sales yang memiliki kinerja paling baik merupakan hal yang penting bagi perusahaan dalam meningkatkan penjualannya. Maka dari itu, untuk mempermudah menilai kinerja sales dibuatlah sebuah sistem dimana perusahaan dapat melihat kinerja sales nya dengan parameter-parameter yang sesuai dengan masing-masing perusahaan. Pengujian metode Regresi Linear untuk memprediksi penjualan sales berikutnya menggunakan koefisien determinasi yang bernilai antara 54.5% sampai 84.5%. Sedangkan pengujian metode Algoritma Apriori, didapatkan 3 kriteria yang dipilih yaitu memenuhi target penjualan, memenuhi jumlah target total transaksi per tahun per tahun, dan memenuhi jumlah transaksi penjualan maksimum per tahun dan 3 nama dan id sales yang menjadi rekomendasi yaitu: id sales 98908 (Ekowati), 98912 (Nini Anggraini), dan 98916 (Ronny Rustan).

KLASIFIKASI KESUBURAN TANAH DENGAN METODE PRINCIPAL COMPONENT REGRESSION Calvin Calvin; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 4, No 2 (2016): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Land be a pleasant to plant life and a layer of earth most outer derived from weathering rocks a have the depth and its own characteristic and organic matter land was the result of weathering plant remains and the animal that mingled with mineral matter another out of the ground at the top layer of soil. The aim of this research is to predict the soil fertility. In this research, the data training and testing is from Landsat 7 Image in Jakarta and Bogor with 6 bands. There are two steps in doing the experiment, training and testing. We use Principal Component Regression in training to find the best model that will be used in testing and return it in the form images in testing. For the evaluation we used Analysis of Variance. Principal Component Regression got 37,35% average error in Jakarta and 35,8% average error in Bogor.

KINERJA METODE MAHALANOBIS DISTANCE YANG DIBENTUK DARI DUA UKURAN PUSAT DAN DUA DISPERSI MULTIVARIAT (UNTUK UKURAN SIMILARITAS KLASIFIKASI IMAGE) Tania Kantacarini; Dyah Erny Herwindiati; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Distance is a space that connects two points or two locations which can be calculated by length and time. Distance is used to measure the similarity of two objects (for example, an image object). An image can be considered to be similar to another image if the similarity size value is small. On the contrary, if the value of the similarity distance between the training object and the object being tested is large, the object can be said to be different or not. In this design, the image classification of Lakes, Forests and Settlements will be carried out by taking the Color feature using the Color Moment extraction method and the Texture feature using the GLCM (Gray Level Co-occurrence Matrix) extraction method and taking the method of calculating the distance between one data and another data that has High similiarity using the Mahalanobis Distance calculation method with two center sizes namely Mean and Median and three multivariate dispersions, namely the covariance matrix formed by the mean center value, the covariance matrix formed by the value of Median, and the covariance matrix formed by the value of Grand Median. From the research conducted, the performance results that can be considered for use are the Mahalanobis Distance with a median center size with a covariance matrix formed by the Median center value with an accuracy of 69.855% and a covariant matrix formed by the Grand Median center value with an accuracy of 69.565%. In this case the percentage is taken from testing images based on color characteristics using the Color Moment extraction method.

APLIKASI E-COMMERCE DENGAN FITUR TOP PRODUCT MENGGUNAKAN METODE PERCEPTRON (STUDI KASUS TOKO KAMERA) Stefanus Alvin Hartono; Bagus Mulyawan; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

The application e-commerce with top product feature using Perceptron method gives result making category of comment from user who gives some comment for product. The process of this method, first calculates the word weight with pre-processing that is using Term Frequency – Inverse Document Frequency (TF-IDF), then calculates comment using perceptron with weight it was specified by the training calculates.The test is done by using data from comments who user input at the product. Top procuct showed when comment was calculate with category from average good or bad reviews. The perceptron method calculate and predict the comment is inputed at the product.

PERANCANGAN SISTEM PENCARIAN LAGU INDONESIA MENGGUNAKAN QUERY BY HUMMING BERBASIS LONG SHORT-TERM MEMORY Henry Hartono; Viny Christanti Mawardi; Janson Hendryli
Jurnal Ilmu Komputer dan Sistem Informasi Vol 9, No 1 (2021): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Song identification dan query by humming is an application that is developed using Mel-frequency cepstral coefficients (MFCC) and Long Short-Term Memory (LSTM) algorithm.The application purpose is to detect and recognize humming from the input data. In this application the humming input will be divided into two parts, namely the training audio and test audio. For the training audio, the training audio will be divided into two process stages, namely recognizing humming and searching for the unique features of a humming audio.To recognize the humming feature, the humming will be processed using the MFCC method. After obtaining a part of the MFCC Features, the MFCC features will be saved as a vector model. The feature that has been extracted will be learned by the LSTM method. For the test audio of the stages carried out as in the training audio, after the MFCC Feature is detected, an introduction will be made based on learning that has been done with the LSTM method to obtain output in the form of a song name that is successfully recognized and detected will be labeled by the application.

Co-Authors Aditya Halimawan Arya Triansyah bagus Mulyawan Caesar Calendo Sumarga Calvin Calvin Chairisni Lubis Christian Christian Christopher Louis Fabian Cynthia Cynthia Dali Santun Naga Desi Arisandi Dyah Erny Herwindiati Erwin Erwin Fendy Augusfian Fredickson Dinata Henry Hartono Hongi Nagaputra Isa Iskandar Ivan Wijaya Jasmine Kezia Halim Kevin kevin Lely Hiryanto Lubby Gennady Mathew Judianto Mitchell Ryu Sopany Nadia Ramadhani Nickolas Cornelius Siantar Oktavianus Oktavianus Rizki Nofrian Wahyudi Rubin Salim Stefanus Alvin Hartono Stenly Tirta Wijaya Stephanie Budianto Stephen Adhikurnia Tania Kantacarini Tania Rizgitta Teny Handhayani Viny Christanti M William Kristianto Wimvy Nanda Tanius

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