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All Journal Jurnal Ilmu Komputer dan Informasi JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems ComTech: Computer, Mathematics and Engineering Applications Jurnal Teknik Informatika (JUTIF) TIERS Information Technology Journal
Lely Hiryanto
Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia
Computer Science & IT Engineering Mathematics Other

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Journal : JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)

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SISTEM REKOMENDASI PERENCANAAN STUDI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA APRIORI DAN NAIVE BAYES (STUDI KASUS FTI UNTAR) Elizabeth Erlsha; Lely Hiryanto
Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 1 (2015): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v3i1.3264

Abstract

The system of student study plan recommendation is a system made using Apriori algorithm and Naive Bayes to create the recommendation of study plan for students in accordance with the maximum load of university credit unit (sks) and have a good chance of passing. The case study that is used in this system is Faculty of Information Technology at Tarumanagara University. Apriori algorithm is used to form a pattern of subjects formed into a frequent pattern tree (FP-tree). Naive Bayes is used to calculate the chances of recommendation passing, using the calculations of grade point average (IPK) and the maximum load of student university credit unit (sks). The system test results show that the system can provide one or more of the study plan recommendation. The percentage of similarity between study plan recommendation offered by the system with student academic record card may vary. This is caused by a list of subjects stored in the pattern of subjects may vary although the total load of stored university credit unit is the same and in fact, students often take subjects less than the maximum load of given university credit unit. Key wordsApriori,FakultasTeknologiInformasiUniversitasTarumanagara, Frequent Pattern Tree, Naive Bayes, Sistem Rekomendasi Perencanaan Studi.
Implementasi Metode Collaborative Filtering Based Untuk Sistem Rekomendasi Buku Fiksi Pharadya Ajeng Swari Sukmawati; Lely Hiryanto; Viny Christanti Mawardi
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.25999

Abstract

A book is the result of someone's work in the form of a collection of papers containing articles intended for publication. One of the benefits of books is that they can open people's horizons and can educate reason, thoughts and faith. Indonesia is ranked 60 out of 61 countries with reading interest problems causing low public interest in reading. One of the factors that can be taken into consideration for interest in reading in Indonesia is the difficulty in finding books to read because the many kinds of books in circulation make it difficult for readers to decide which books to read, besides that readers only want to read books with the best reputation. The purpose of creating a book recommendation system is to make it easier to find fiction-type books to read. The data used in this design are book data and rating data from Kaggle. This design uses one of the recommendation system methods, namely collaborative filtering. Collaborative filtering is a recommendation method that calculates similarity between items by users to make choices. The system will recommend 5 books according to the book title that the user will input.
Penerapan Metode K-Means Clustering Untuk Menentukan Pola Penjualan Kue Pada Alfaza Bakery Riki Yohanes Hendriyanto; Lely Hiryanto
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26001

Abstract

The home industry is a type of small-scale business activity that is often found in villages and around houses, both in urban and rural areas. Starting from an association of the same people who studied to pursue the field of making cakes and bread and who then wanted to expand the sales area and create jobs for residents. the obstacle faced is ignorance of the products that are purchased the most and in which areas certain products run out the fastest, it is necessary to do data mining analysis using the clustering method. The K-Means method is a data clustering method using observation based on the similarity of the objects studied. A cluster is a collection of data that has similarities in its members or is different from other groups, clusters are used to minimize variation within a cluster and maximize variation between clusters, in other words data that has attribute similarities between one another and attribute differences to other clusters, determines the right cluster by using the elbow method which can maximize the quality of clusters so that the clusters are more varied. The results of testing this study with the elbow method obtained the right number of 4 clusters, then the clustering results with the most sales were obtained in cluster 3, cluster 1 with moderate sales, cluster 0 with few sales and cluster 2 with the least sales.
PREDIKSI CURAH HUJAN DI KABUPATEN BADUNG, BALI MENGGUNAKAN METODE LONG SHORT-TERM MEMORY Brando Dharma Saputra; Lely Hiryanto; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26002

Abstract

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.
Aplikasi Monitoring Tunggakan Uang Kuliah Mahasiswa Non Aktif Di Universitas Tarumanagara Menggunakan Metode Naive Bayes Timothy Reynaldi; Lely Hiryanto; Darius Andana Haris
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26005

Abstract

Universitas Tarumanagara memiliki dua status mahsiswa, yaitu mahasiswa aktif dan mahasiswa non aktif. Saat ini, bidang administrasi di Universitas Tarumanagara belum memiliki sistem yang baik untuk menangani tunggakan uang kuliah dari mahasiswa non aktif. Tujuan dari perancangan Aplikasi Monitoring Tunggakan Uang Kuliah Mahasiswa Non Aktif ini adalah untuk memperbaiki dan memudahkan user untuk memonitoring tunggakan uang kuliah dari mahasiswa non aktif di Universitas Tarumanagara. Aplikasi ini menggunakan metode Naive Bayes. Penerapan dari metode Naive Bayes ini berfungsi untuk menghitung probabilitas kemungkinan mahasiswa Universitas Tarumanaga yang non aktif selama tiga semester berturut-turut harus di keluarkan atau tidak. Hasil dari penerapan metode Naive Bayes ini berhasil untuk menampilkan output prediksi untuk dikeluarkan atau dilanjutkannya mahasiswa yang sudah non aktif selama tiga semester berturut-turut. Hasil dari pengujian fungsional aplikasi menggunakan mendapatkan output sukses untuk pengetesan pada semua halaman yang di uji dan metode pengambilan keputusan dari aplikasi ini memiliki akurasi untuk prediksi tindakan pengambilan keputusan sebesar 91%.
IMPLEMENTASI AES UNTUK KEAMANAN APLIKASI FORMULIR ONLINE Andri Firnandius; Lely Hiryanto
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i2.26011

Abstract

Google Forms software is an online application which users can create form for various purpose. The application can store information or data that has been provided by the form fillers. The form fillers are merely identified by their institutional email's domain or those with the access link to the make response for each question provided in an online form. The use of third-party applications certainly reduces the sense of trust in the security of the data provided. Therefore, a digital form application design was created with the Advanced Encryption Standard (AES). The aim is to maintain the security of the data provided by the form filler and ensure that the fillers are those with the authority.
Co-Authors Alfine Candra Cuaca Anak Agung Gede Sugianthara Andre Widjaya Andri Firnandius Andri Muliawan Ardhytia Satria Nugraha Arnold Pramudita Tjiawi Aurelia Bagus Mulyawan bagus Mulyawan Bobby Tumbelaka Bobby Tumbelaka Brando Dharma Saputra Chairisni Lubis Chandra Wijaya Chandra Wijaya Chandra Wijaya Chintia Yusnita Violetta Darius Andana Haris Dedi Trisnawarman Desi Arisandi Dya Erny Herwindiati Dyah Erny Herwindiati Dyah Erny Herwindiati Elizabeth Erlsha Elizabeth Erlsha, Elizabeth Ericko Satyagraha Ericko Satyagraha Farenco Farenco Farenco Farenco Ferryanto Ferryanto Ferryanto Ferryanto Fika Alfiani Frankie Frankie Frankie Frankie, Frankie Fransisca Regina Fransisca Regina, Fransisca Gabriel Fransisco Gabriel Fransisco, Gabriel Grimaldi Suryadi Grimaldi Suryadi Gunadi Gan Gunadi Gan Harprori Patti Irawati Djajadi Irawati Djajadi Isa Iskandar Jacklin Sinthia Thio James Ariel Gunawan James Ariel Gunawan, James Ariel Janson Hendryli Jason Djatmiko Josselyn Sinthia Thio Karendef Karendef Kristianto, Hans Kurniawan Sulianto Lee, Viciano Lina Lina Listovie Cavito Mariana - Mariana Mariana Martono Darsono Martono Darsono Mishelle Tirtajaya Winartha Nadia Yanitra Nadya Yanitra, Nadya Pharadya Ajeng Swari Sukmawati Ratchagit, Manlika Renaldo Ali Renaldo Ali, Renaldo Riki Yohanes Hendriyanto Rionaldy Trisaputra Rosalinda . Rosalinda Rosalinda Satrya N. Ardhytia Stephanie Budianto Stephen Yan Putra Halim, Stephen Yan Putra Stevy Lie Stevy Lie, Stevy Sufisan Sufisan TATI NURHAYATI Teny Handhayani Timothy Reynaldi Tony Tony Tony Tony TRI SUTRISNO Vina Tandean Viny Christanti M Wirawan, Andhika Putra Yunita Yunita Yunita Yunita