Article Per Year (5 Year)
p-Index From 2021 - 2026
4.827
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Kursor Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri Jurnal Mantik Penusa JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Informatika Kaputama (JIK) Jurnal Manajemen Informatika Jayakarta Archive: Jurnal Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Applied Business and Technology Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Deny Jollyta
Institut Bisnis Dan Teknologi Pelita Indonesia
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Public Health Social Sciences

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Mengatasi Kelemahan Internal Menggunakan Mc-Kinsey 7s Untuk Peningkatan Standar Mutu Pendidikan Deny Jollyta; Relita Buaton; N Novriyenni; Achmad Fauzi
Archive: Jurnal Pengabdian Kepada Masyarakat Vol. 1 No. 1 (2021): Desember 2021
Publisher : Asosiasi Pengelola Publikasi Ilmiah Perguruan Tinggi PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.994 KB) | DOI: 10.55506/arch.v1i1.6

Abstract

Mutu sebuah sekolah ditandai dengan berjalannya sistem penjaminan mutu di internal sekolah. Pencanangan Sekolah Menengah Kejuruan Pusat Keunggulan (SMK PK) oleh pemerintah menguatkan kenyataan bahwa penjaminan mutu sekolah sangat diperlukan dalam mencapai Standar Mutu Pendidikan. Terlaksananya penjaminan mutu sekolah merupakan early warning system untuk memperbaiki kesalahan sebelum situasi semakin parah. Kesulitan yang terjadi dalam pencapaian standar adalah kurangnya kesadaran sekolah terhadap kelemahan diri sendiri. Pemicu kelemahan tidak mampu diatasi dan cenderung diabaikan. Studi ini bertujuan untuk menghasilkan sebuah model penyelesaian kelemahan internal sekolah dengan Mc-Kinsey 7s dalam mencapai mutu melalui gambaran sejumlah indikator yang disusun dalam bentuk angket. Data angket diolah menggunakan SPSS dengan hasil 33,33% dari indikator berada pada ranah Cukup, Kurang dan Sangat Kurang. Kelemahan pada indikator ini diperkuat dengan 7 elemen dari model Mc-Kinsey 7s untuk dihasilkan penyelesaian. Diharapkan penguatan melalui integrasi 7 elemen Mc-Kinsey dapat mengatasi kelemahan internal sekolah dalam menuju SMK PK yang berkualitas dan bermartabat.
WEB-BASED CLUSTER OPTIMIZATION USING K-MEDOIDS AND DAVIES BOULDIN INDEX Ryan Christian; Deny Jollyta
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 9, No 1 (2022): Desember 2022
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v9i1.1855

Abstract

Abstract: Clustering data has always been a fascinating subject to research numerous perspectives. A variety of knowledge is produced by the calculating process utilizing various algorithms. The genesis of cluster optimization is based on differences of opinion about the cluster's results. In general, cluster and optimization findings are generated using software such as Matlab, RapidMiner, and programming languages like Python. Users, however, have not been satisfied with the results so far. The various outcomes are the primary motivations for continuing to create and develop applications. The goal of this research is to create an application that can evaluate cluster data using the K-Medoids method, which can then be further optimized using the Davies Bouldin Index (DBI). Because the target application is students and lecturers who use it in learning and observers of the cluster field, the application can indeed be accessible through a browser to make it easier to use. For ease of using it, the program is available on both desktop and mobile platforms. Through separately created applications, it is intended that this research will give an alternative to clustering and optimization.            Keywords: application, cluster, dbi, k-medoids, optimization  Abstrak: Clusterisasi data selalu menjadi topik yang menarik untuk dikembangkan dari berbagai sisi. Proses perhitungannya yang menggunakan berbagai algoritma menghasilkan knowledge yang beragam. Perbedaan pendapat terhasil hasil cluster menjadi dasar munculnya optimalisasi cluster. Umumnya hasil cluster dan optimalisasi diperoleh dari pengolahan menggunakan aplikasi yakni Matlab, RapidMiner, dan bahasa pemrograman seperti Pyhton. Namun demikian hasil yang muncul belum mampu memuaskan pengguna. Hasil yang berbeda menjadi alasan utama pembuatan maupun pengembangan aplikasi masih terus dilakukan. Penelitian ini bertujuan untuk membangun sebuah aplikasi yang dapat memproses data cluster menggunakan algoritma K-Medoids untuk selanjutnya dioptimalisasi dengan Davies Bouldin Index (DBI). Untuk memudahkan penggunaan, aplikasi dapat diakses pada browser karena target aplikasi adalah mahasiswa dan dosen yang menggunakan pada pembelajaran serta pemerhati bidang cluster. Aplikasi dirancang pada platform desktop dan mobile demi memudahkan pengaksesan. Diharapkan, penelitian ini memberikan alternatif dalam proses clusterisasi dan optimalisasi melalui aplikasi yang dirancang mandiri. Kata kunci: aplikasi; cluster; dbi; k-medoids; optimalisasi
ANALISIS PENERAPAN NORMALIZED WEB DISTANCE: TINJAUAN KASUS GOOGLE DAN COMPRESSION DISTANCE DENY JOLLYTA
Jurnal Informatika Kaputama (JIK) Vol 5 No 1 (2021): Volume 5, Nomor 1, Januari 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v5i1.320

Abstract

Currently, various techniques for measuring the proximity between two objects in the internet network are continuously being developed. The objects in question are in the form of concepts, e-mails, words, and so on. Normalized Web Distance (NWD) has proven to be a simple, yet powerful measure of the semantic linkages between the two concepts. NWD has several approaches according to the object being measured, such as Normalized Google Distance (NGD) and Normalized Compression Distance (NCD). NGD and NCD have a way of determining similarity and calculating the distance to find the similarity of two different measuring objects. This paper shows and provides information on the performance of the two NWD approaches, namely NGD and NCD to facilitate understanding of the use of NGD and NCD on various problems. Correct understanding can put the NGD and the NCD in the right case.
PENGOPTIMALAN PENGUKURAN BREGMAN DIVERGENCES MENGGUNAKAN DAVIES BOULDIN INDEX Deny Jollyta; Muhammad Siddik; Johan Johan; Gustientiedina Gustientiedina
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 7 No 1 (2023)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penentuan cluster optimal ini seringkali ambigu karena dihasilkan dari beberapa kelompok. Pemilihan informasi dari kelompok mana yang akan digunakan oleh pengguna menjadi masalah tersendiri karena menyangkut pembuatan kebijakan. Davies Bouldin Index (DBI) merupakan teknik evaluasi cluster untuk menentukan jumlah cluster yang optimal dan didukung dengan pengukuran jarak yang tepat. Penelitian ini bertujuan untuk mendapatkan cluster melalui teknik DBI yang diterapkan pada pengukuran Bregman Divergences, Mahalano dan Square Euclidean Distance, menggunakan algoritma K-Means dan K-Medoids. Hasil pengujian ditunjukkan melalui beberapa indikator yang digunakan sebagai tolak ukur untuk mengetahui kinerja Bregman Divergences dalam menentukan jumlah cluster seperti korelasi, algoritma cluster yang digunakan, pola DBI, hasil DBI, k-optimal dan waktu yang dibutuhkan untuk pengujian. Melalui kedua algoritma clustering tersebut, jarak Mahalano dapat menghasilkan pola pengelompokan yang konsisten dan teknik pengukuran Square Euclidean Distance berhasil menunjukkan performa DBI terbaik yang menempatkan k=2 sebagai cluster optimal, nilai DBI terendah sebesar 0,882 dan 1,030 pada waktu pengujian. selama 0 detik.
Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases Dadang Priyanto; Ahmad Robbiul Iman; Deny Jollyta
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1544.262-270

Abstract

Indonesia, with its very large population, is a potential market for drugs trafficking. Hence, seriousness is needed in cracking down or preventing drug trafficking. Narcotics are substances or drugs that can cause dependence or addicted and other negative impacts on users. The problem is that drug users do not realize and even ignore diseases caused by drug addiction. The diseases can be life-threatening for users, such as inflammation of the liver, heart disease, hypertension, stroke, and others. The prevalence rate of drug abuse in West Nusa Tenggara (NTB) is included in the high category, reaching 292 cases or around 37.24% cases. This study aimed to create an application that can classify various diseases of drug users using the naïve bayes and KNN methods. The results of this study indicated that there was a very close relationship between drug users and various deadly diseases. The prediction results showed that the naive bayes method provided a prediction accuracy of 94.5% while the KNN showed a prediction accuracy of 92.5%. This shows that the naive bayes method provides better predictive performance than the KNN in the data set of drug addicts in NTB.
CLASSIFICATION OF FAKE NEWS IN INDONESIAN LANGUAGE USING SUPPORT VECTOR MACHINE METHOD Andreas Halim Tandiano; Deny Jollyta
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 2 (2024): Maret 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2895

Abstract

Abstract: Since information and communication technology has become ingrained in our daily lives, it has become easier to access information. However, there are some concerns. One of them is about fake news. The aim of this study is to develop an Indonesian system for detecting false news by utilizing news headlines. The methods used are linear kernel support vector ma- chine and n-gram. According to the findings of the performance test that was carried out, the linear kernel support vector machine model employing the term frequency inverse document frequency unigram feature performs better than utilizing bigram. The precision value generated from the model performance test is 1.00. This means that the degree of accuracy in matching the requested information regarding fake news detection with the answers provided by the system is very good. Then the recall value generated is 0.99. This means the linear kernel support vector machine model using unigram news features is very effective for detecting fake news according to the text classification approach.            Keywords: classification; fake news; n-gram; support vector machine Abstrak: Dengan adanya integrasi teknologi informasi dan komunikasi dalam kehidupan mem- buat kemudahan dalam mengakses informasi. Walaupun demikian, terdapat kekhawatiran akan beberapa hal. Salah satu di antaranya adalah berita palsu. Tujuan penelitian ini adalah merancang sistem deteksi berita palsu berbahasa Indonesia berdasarkan judul berita. Metode yang digunakan adalah Support Vector Machine kernel linier dan n-gram. Berdasarkan hasil uji performa, model Support Vector Machine kernel linier yang menggunakan fitur term frequency inverse document frequency unigram menunjukkan kinerja yang lebih baik dibandingkan bi- gram. Nilai precision yang dihasilkan dari uji performa model sebesar 1,00. Ini berarti derajat akurasi dalam mencocokkan informasi yang diminta mengenai deteksi berita palsu dengan ja- waban yang diberikan oleh sistem sangat baik. Kemudian nilai recall yang dihasilkan sebesar 0,99. Ini berarti model Support Vector Machine kernel linier dengan menggunakan fitur berita unigram sangat efektif untuk mendeteksi berita palsu menurut pendekatan teks klasifikasi. Kata kunci: klasifikasi; berita palsu; n-gram; support vector machine 
Membangun Mutu Melalui Peningkatan Kualitas Perangkat Lunak Menggunakan Metode TELOS Deny Jollyta; Gusrianty Gusrianty; Alyauma Hajjah; Wahyu Joni Kurniawan; Gustientiedina Gustientiedina; Johan Johan; Dwi Oktarina; Hutri Rizkiyah Alda; Hadi Dwi Putra; Darmanta Sukrianto; Loneli Costaner
Archive: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 2 (2024): Juni 2024
Publisher : Asosiasi Pengelola Publikasi Ilmiah Perguruan Tinggi PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55506/arch.v3i2.108

Abstract

Seiring dengan bertambahnya data dan kebutuhan operasional kerja, fungsi perangkat lunak memerlukan pembaharuan dan peningkatan. Pentingnya kegiatan pengabdian ini dilakukan karena permasalahan muncul saat tidak memahami cara maupun metode yang tepat untuk meningkatkan kualitas perangkat lunak, termasuk oleh Sekolah Menengah Kejuruan Negeri (SMKN) di Pekanbaru. Pengabdian ini bertujuan untuk memberikan pemahaman sekaligus pelatihan kepada guru dan siswa SMKN di Pekanbaru tentang metode yang tepat untuk peningkatan kualitas perangkat lunak. Metode yang diusulkan adalah Technical, Economic, Legal, Operational dan Schedule atau disebut TELOS. Metode ini digunakan untuk menentukan kelayakan terhadap kualitas sistem informasi akademik sekolah yang perlu ditingkatkan. Sistem dianalisis melalui 5 aspek TELOS secara objektif melalui 50 responden pengguna sistem dari lingkungan sekolah. Hasil TELOS menunjukkan bahwa sistem informasi akademik sekolah layak untuk ditingkatkan kualitasnya dengan rata-rata nilai TELOS adalah 7.812.
N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System Jollyta, Deny; Gusrianty, Gusrianty; Prihandoko, Prihandoko; Sukrianto, Darmanta
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1770.398-404

Abstract

The modern technological advancements have made it simpler for fake news to circulate online. The researchers have developed several strategies to overcome this obstacle, including text classification, distribution network analysis, and human-machine hybrid methods. The most common method is text categorization, and many researchers offer deep learning and machine learning models as remedies. An Indonesian language fake news detection system based on news headlines was developed in this work using the Support Vector Machine (SVM) kernel and n-gram. The objective of this research is to identify the model that produces the best performance outcomes. The system deployment on the web will employ the model that produces the greatest outcomes. According to the research findings, the linear kernel SVM algorithm produces the best results, with an accuracy value of 0.974. Furthermore, the bigram feature used in the development of a classification model does not increase the precision of fake news identification in Indonesian. Utilizing the unigram function yields the most accurate results.
THE INFLUENCE OF DATA CATEGORIZATION AND ATTRIBUTE INSTANCES REDUCTION USING THE GINI INDEX ON THE ACCURACY OF THE CLASSIFICATION ALGORITHM MODEL Willy Fernando; Jollyta, Deny; Dadang Priyanto; Dwi Oktarina
Jurnal Ilmiah Kursor Vol. 12 No. 3 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i3.372

Abstract

Numerical data problems are typically caused by a failure to comprehend the data and the outcomes of its processing. In order to give richer context and a deeper understanding of the facts, numerical data must be transformed into categories. On the other hand, changes in data have a significant impact on the analysis's outcomes. The purpose of this study is to see how transforming numerical data into categories affects the model produced by the classification algorithms. The dataset used in this study is the Maternal Health Risk. Categorization refers to formal arrangements. Categorization is also accomplished by using the Gini Index to limit the number of instances of an attribute. The classification is carried out using the Random Forest (RF), K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms to produce a model. The influence of data modifications to model can be observed in the confusion matrix with 5 different data splitting. The study results suggested that changing numerical data to categories data significantly improved the performance of the SVM model from 76.92% to 80.77% at a data splitting percentage of 95/5.
PARAMETER ASOSIASI UNTUK MENENTUKAN KORELASI JURUSAN DAN INDEKS PRESTASI KUMULATIF Buaton, Relita; Jollyta, Deny; Mawengkang, Herman; Zarlis, Muhammad; Effendi, Syahril
Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (892.061 KB) | DOI: 10.33480/pilar.v15i1.285

Abstract

One of the problems in higher education is the mistake of prospective students in majors selection. This is caused by not paying attention to the suitability of the major in the original school with the chosen major in higher education so that it impacts not only non optimal processing and learning outcomes, such as the low GPA, but also on social life, such as increasing unemployment. The selection of the right major is very important and to help prospective students in choosing it requires an online system that can be accessed by everyone and select original school majors to see conformity with majors in higher education. This system uses association rules and parameters of support and confidence in data mining. The purpose of this research is to determine the correlation between majors in the original school, majors in higher education and the achievement of the GPA through the use of support and confidence parameters that process the knowledge base in the form of an alumni database on the online system created. Training or testing was conducted on 10,254 data in the database and produced new information and knowledge that between the majors of the original school, the choice of majors in higher education and GPA had a strong correlation with the value of confidence reaching 100%.
Co-Authors Adiya, M. Hasmil ahmad kamal, ahmad Ahmad Robbiul Iman Akwilla, Hiskia Alyauma Hajjah Andreas Halim Tandiano Astri , Renita Ayu Dasriani, Ni Gusti Buaton, Relita Dadang Priyanto Dadang Priyanto Dafwen Toresa Darmanta Sukrianto Darmanta Sukrianto Des Suryani Des Suryani Dewi Nasien Dixsen Dixsen Dwi Oktarina Dwi Oktarina Dwi Oktarina Eri Yanto Erick Santoso Erlin Erlin Fauzi, Achmad Fenly, Fenly Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty, Gusrianty Gustientiedina Hadi Dwi Putra Hairani Hairani Herman Mawengkang Hutri Rizkiyah Alda Irvan Firnando Jeffry Kody Johan Johan Johan Johan Kadim, Lina Arliana Nur Kody, Jeffry Lasman Toni Loneli Costaner Marlim, Yulvia Nora Muhammad Siddik Muhammad Siddik Muhammad Zarlis Muhammad Zarlis, Muhammad Mukhsin Mukhsin N Novriyenni Oktariana, Dwi Pratama, Teddy Prihandoko . Prihandoko Prihandoko Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ramdhan, William Ricky Wijaya Rivensin Rivensin Ryan Christian Sabna, Eka Saputra, Fandri Siddik, Muhammad Sukrianto, Darmanta Surianto Surianto Syahril Effendi Tandiano, Andreas Halim Taslim Taslim Teddy Pratama Tony Tony Toresa, Dafwen Triwijoyo, Bambang Krismono Vincent Wijaya Wahyu Joni Kurniawan Wilda Susanti Willy Fernando