Articles
<![CDATA[PENJADWALAN KELAS MATAKULIAH MENGGUNAKAN VERTEX GRAPH COLORING DAN SIMULATED ANNEALING ]]>
<![CDATA[Mariana Mariana]]>;
<![CDATA[Lely Hiryanto]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 1, No 1 (2013): Jurnal Ilmu Komputer dan Sistem Informasi ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v1i1.3082
<![CDATA[Makalah ini membahas tentang penggabungan metode vertex graph coloring dan simulated annealing dalam menyusun jadwal matakuliah. Penggabungan ini ditujukan untuk mengetahui seberapa layak dan optimal penjadwalan yang dibuat dari gabungan kedua metode ini. Vertex Graph Coloring adalah metode pemberian warna pada simpul dengan mencari vertex tetangga dan tidak bertetangga, sehingga vertex yang bertetangga akan diberi warna yang sama dan vertex yang tidak bertetangga akan diberi warna baru yang berbeda. Simulated Annealing (SA) adalah teknik optimalisasi numerik dengan prinsip thermo-dynamic. Kinerja SA sangat bergantung pada solusi awal, lingkungan pencarian dan proses pendinginan. Vertex Graph Coloring (VGC) bekerja untuk memenuhi seluruh hard constraints dan Simulated annealing bekerja untuk meneruskan proses penjadwalan dengan mengoptimalkan penjadwalan tersebut. Hasil penjadwalan yang diperoleh dari penggabungan kedua metode ini adalah menghasilkan penjadwalan yang visible dan optimal meskipun beberapa ketentuan soft constraints masih terlanggar. ]]>
<![CDATA[¬Sistem Dokumentasi Kurikulum, Penawaran Mata Kuliah, dan Alokasi Dosen Pengajar (Studi Kasus Fakultas Teknologi Informasi Universitas Tarumanagara) ]]>
<![CDATA[Stevy Lie]]>;
<![CDATA[Lely Hiryanto]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 1 (2015): Jurnal Ilmu Komputer dan Sistem Informasi ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v3i1.3288
<![CDATA[System of curriculum management, class offers, and allocation of lecturers is made by using Naive Bayes to produce curriculum data, class offers, and allocation of a class lecturer. The sample case is Faculty of Information Technology at Tarumanagara University. Naive Bayes is used to acquire class opened and the number of classes that need to be offered in the new semester. The test results show has success to do manufacturing system, however can only offer per class. Kata KunciAlokasi Dosen Pengajar, Fakultas Teknologi Informasi Universitas Tarumanagara, Naive Bayes, Penawaran Mata Kuliah, Dokumentasi Kurikulum]]>
<![CDATA[APLIKASI SECURE VIRTUAL OFFICE MENGGUNAKAN METODE AES, RSA, DAN MD5 ]]>
<![CDATA[Jason Djatmiko]]>;
<![CDATA[Lely Hiryanto]]>;
<![CDATA[Bobby Tumbelaka]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 2, No 1 (2014): Jurnal Ilmu Komputer dan Sistem Informasi ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v2i1.3171
<![CDATA[The developed application consists of three security features: MD5 hash that is used for login and registration purposes, RSA and AES encryption for uploaded files, and AES encryption for fetched e-mails which is performed before storing them to the database. The other features such as meetings management, task management, groups, discussion, etc. are designed to provide basic office functions to connected users. Each entry to the database is fully available to authorized administrators. Therefore, administrators will be able to maintain data if the need arises. Testing results show that the application has managed to withstand hacking attempts using SQL Injection. It also has been tested to process and secure user-inputted files and fetched e-mails. However, there is still a problem that arises whenever the user of the application tries to configure the mail client for Yahoo! Mail account. Key wordsAES, MD5, RSA, Secure Virtual Office.]]>
<![CDATA[SISTEM REKOMENDASI PERENCANAAN STUDI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA APRIORI DAN NAIVE BAYES (STUDI KASUS FTI UNTAR) ]]>
<![CDATA[Elizabeth Erlsha]]>;
<![CDATA[Lely Hiryanto]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol 3, No 1 (2015): Jurnal Ilmu Komputer dan Sistem Informasi ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v3i1.3264
<![CDATA[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.]]>
<![CDATA[PREDIKSI MASA STUDI MAHASISWA DENGAN VOTING FEATURE INTERVAL 5 PADA APLIKASI KONSULTASI AKADEMIK ONLINE ]]>
<![CDATA[Andre Widjaya]]>;
<![CDATA[Lely Hiryanto]]>;
<![CDATA[Teny Handhayani]]>
<![CDATA[ Computatio : Journal of Computer Science and Information Systems Vol 1, No 1 (2017): Computatio : Journal of Computer Science and Information Systems ]]>
Publisher : <![CDATA[Faculty of Information Technology, Universitas Tarumanagara ]]>
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DOI: 10.24912/computatio.v1i1.238
<![CDATA[Aplikasi prediksi masa studi mahasiswa merupakan aplikasi yang mengimplementasikan metode Voting Feature Interval 5 pada fitur prediksi masa studi mahasiswa. Hasil prediksi masa studi akan digunakan sebagai masukkan bagi dosen penasehat akademik dan ketua program studi untuk menyusun rencana studi yang tepat bagi mahasiswa. Studi kasus yang digunakan dalam aplikasi adalah prediksi masa studi mahasiswa Program Studi Teknik Informatika, di salah satu perguruan tinggi swasta di Jakarta, angkatan 2013 dan 2014. Adapun data pembelajaran yang digunakan adalah data nilai mahasiswa, program studi Teknik Informatika di perguruan tinggi swasta tersebut angkatan 2008 sampai 2012. Metode Voting Feature Interval 5 bekerja dengan mengklasifikasikan data nilai mahasiswa berdasarkan nilai vote yang terbentuk dari hasil pembelajaran data latih. Hasil pengujian aplikasi menunjukkan bahwa aplikasi berhasil menghasilkan prediksi masa studi bagi mahasiswa dengan tingkat akurasi yaitu sebesar 73,33%.]]>
<![CDATA[PERANCANGAN APLIKASI PENDETEKSI TINGKAT KESAMAAN ANTAR DOKUMEN DENGAN ALGORITMA WINNOWING ]]>
<![CDATA[Arnold Pramudita Tjiawi]]>;
<![CDATA[Dyah Erny Herwindiati]]>;
<![CDATA[Lely Hiryanto]]>
<![CDATA[ Computatio : Journal of Computer Science and Information Systems Vol 2, No 1 (2018): Computatio : Journal of Computer Science and Information Systems ]]>
Publisher : <![CDATA[Faculty of Information Technology, Universitas Tarumanagara ]]>
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DOI: 10.24912/computatio.v2i1.1918
<![CDATA[Perancangan aplikasi pendeteksian tingkat kesamaan antar dokumen ini dimaksudkan untuk menyimpan data tesis mahasiswa dan aplikasi dapat digunakan untuk membandingkan data skripsi mahasiswa dengan data baru yang ada. Hasil dari proses deteksi adalah persentase tingkat kesamaan. Algoritma yang digunakan dalam merancang aplikasi ini adalah Algoritma Winnowing. Algoritma ini termasuk dalam salah satu metode fingerprinting dokumen. Algoritma ini menggunakan rolling hashing untuk melakukan proses hashing dan menggunakan koefisien jaccard untuk menghitung tingkat kemiripan. Dalam pengujian aplikasi ini dengan data dummy diketahui bahwa aplikasi ini sangat bergantung pada urutan masing-masing lokasi sub string. Aplikasi ini juga telah diuji untuk membandingkan sejumlah data skripsi yang ada dengan persentase tingkat kesamaan di bawah 30 persen. Aplikasi ini tidak bisa menentukan apakah sebuah dokumen plagiat atau tidak, namun aplikasi ini bisa memberikan informasi berupa persentase tingkat kesamaan dan persentase dapat digunakan oleh pihak yang memiliki wewenang untuk menentukan.]]>
<![CDATA[Predicting and Analyzing the Length of Study-Time Case Study: Computer Science Students ]]>
<![CDATA[Teny Handhayani]]>;
<![CDATA[Lely Hiryanto]]>
<![CDATA[ ComTech: Computer, Mathematics and Engineering Applications Vol. 8 No. 2 (2017): ComTech ]]>
Publisher : <![CDATA[Bina Nusantara University ]]>
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DOI: 10.21512/comtech.v8i2.3756
<![CDATA[The length of study-time is one of the important issues in higher education. The goal of this research was to predict and analyze the length of studytime in the early stage of Computer Science students in X University. The research proposed Mutual Information (MI) as feature selection method and Support Vector Machine (SVM) as a classification method. There were two different sections of the experiments. The first experiment used two class targets that were grouped in ‘on time group’ and ‘late group’. The experiment result shows that the proposed method produces accuracy around 85%. The second experiment used three class targets, ‘on time group’, ‘late group’, and ‘very late group’. The experiment result of the proposed method produces accuracy around 80%. Mutual Information (MI) does not only successfully raise the accuracy but also uncovers the relationship between subjects and the class targets.]]>
<![CDATA[ANALISIS KOMPLEKSITAS MASALAH PENJADWALAN SEMINAR ILMIAH ]]>
<![CDATA[Lely Hiryanto, M.Sc.]]>;
<![CDATA[Tony Tony]]>;
<![CDATA[Dian Anggraini Cahyaningtyas]]>
<![CDATA[ Computatio : Journal of Computer Science and Information Systems Vol. 6 No. 2 (2022): Computatio: Journal of Computer Science and Information Systems ]]>
Publisher : <![CDATA[Faculty of Information Technology, Universitas Tarumanagara ]]>
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DOI: 10.24912/computatio.v6i2.21047
<![CDATA[Penjadwalan seminar ilmiah skala besar atau conference scheduling adalah salah satu masalah penjadwalan yang kompleks. Ada lima faktor utama yang dipertimbangkan ketika menyusun jadwal seminar ilmiah: (i) jumlah penyaji makalah, moderator dan pembicara tamu, (ii) kesediaan waktu moderator dan pembicara tamu, (iii) jumlah ruang seminar, (iv) jumlah sesi seminar dan (v) jumlah penyaji makalah yang dapat dijadwalkan dalam satu sesi seminar. Makalah ini menganalisis kompleksitas dari masalah seminar dengan mempertimbangkan kelima faktor tersebut. Analisis didasarkan pada penurunan dari tiga masalah yang telah terbukti memiliki kompleksitas Non-deterministic Polynomial Hard (NP Hard).]]>
<![CDATA[Implementasi Metode Collaborative Filtering Based Untuk Sistem Rekomendasi Buku Fiksi ]]>
<![CDATA[Pharadya Ajeng Swari Sukmawati]]>;
<![CDATA[Lely Hiryanto]]>;
<![CDATA[Viny Christanti Mawardi]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v11i2.25999
<![CDATA[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.]]>
<![CDATA[Penerapan Metode K-Means Clustering Untuk Menentukan Pola Penjualan Kue Pada Alfaza Bakery ]]>
<![CDATA[Riki Yohanes Hendriyanto]]>;
<![CDATA[Lely Hiryanto]]>
<![CDATA[ Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 2 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi Universitas Tarumanagara ]]>
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DOI: 10.24912/jiksi.v11i2.26001
<![CDATA[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.]]>
