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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SemanTIK : Teknik Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika EKONOMIS : Journal of Economics and Business IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi Informatik : Jurnal Ilmu Komputer SEINASI-KESI Jurnal Riset Informatika JURNAL MANAJEMEN BISNIS JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Kajian Manajemen dan Wirausaha Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Sisfotek Global Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Ticom: Technology of Information and Communication Telematika MKOM SmartComp Jurnal Ticom: Technology of Information and Communication Teknologi : Jurnal Ilmiah Sistem Informasi
Deni Mahdiana
Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Budi Luhur
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Implementation of Random Forest Algorithm for Graduation Prediction Riskiyono, Fajar; mahdiana, Deni
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13750

Abstract

University also has responsibility for the period of study taken by students in accordance with the level of education taken. The prediction of student study duration is designed to support the study program in guiding students to graduate on time. In this problem, data mining techniques can be applied to make predictions, namely by using the Random Forest classification method. The stages used in this study are data collecting, namely collecting student data, the data selection stage of 300 students with 5 (five) input data attributes including personal data (gender, age, marital status, job status) and academic data (grade) and 1 (one) attribute as an output containing choices about on time and late. The next stage is preprocessing with the aim of eliminating duplication, noise, and missing values, the stage of data transformation by normalizing age attributes (young and old), grade (large and small). Then the data split stage 3 times, namely 50/50, 40/60, and 30/60, the modeling stage with random forest, and finally, the evaluation stage by analyzing the confusion matrix consisting of accuracy, precision, and recall. The results of the study show that the proposed model can do well with predictions, that is, with the same results for all three data splits. The test value is 100% accuracy, 100% recall, and 100% precision. With this value, the success rate for predicting the timeliness of student graduation will be more accurate
Prediksi Potensi Keterlambatan Pembayaran Biaya Kegiatan Sekolah Menggunakan Algoritma Naïve Bayes Solehan, Solehan; Sugiarto, Sugiarto; Mahdiana, Deni; Kharmytan, Yan Baktra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7831

Abstract

Tuition fees are one of very important component in the implementation of education and school development, because tuition fees are the main requirement to be able to implement school programs that have been designed in the one school year activity plan, apart from that, school fee is also used for maintenance or development of school facilities and infrastructure. Cempaka Vocational School is a private school in Central Jakarta, region two which requires its students to pay school activity fees by paying for each activity (7 days before the activity takes place) or in installments every month (total activity costs for one year divided by twelve ). Meanwhile, based on data obtained from the school treasurer, there were 23.2% arrears in the 2020/2021 school year and 38.7% arrears in the 2021/2022 school year (The data used in this research was taken from student payment data for the 2020/2021 and 2021/2022 school years ) which has not been resolved by students, this will become a big problem for Cempaka Vocational School if a solution is not immediately found to overcome this problem. The aim of this research is to build a prediction system using the Naïve Bayes method which will produce an accurate or late classification in paying school activity fees to be used as a recommendation in policy making and finding solutions early on so that there are no delays in paying school fees by students. /i. The results of this research produced an accuracy of 70.83%, precision of 70.59% and recall of 85.71 so that it could predict delays in school activity costs according to the needs of Cempaka Vocational School.
Penerapan Algoritma Naïve Bayes Untuk Melakukan Analisis Sentimen Pada PT Pos Indonesia (Persero): Application of Nave Bayes Algorithm to Perform Sentiment Analysis at PT Pos Indonesia (Persero) Manarul Haikal Casandy; Deni Mahdiana
KRESNA: Jurnal Riset dan Pengabdian Masyarakat Vol 2 No 2 (2022): Jurnal KRESNA November 2022
Publisher : DRPM Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/jk.v2i2.51

Abstract

Pos Indonesia is the oldest shipping service that is widely known to the public, so it has different opinions on the performance of postal expeditions. The author identifies the problem in this research, namely the public's sentiment on the services of PT. Pos Indonesia (Persero) which is found on the Twitter social media platform and the number of positive or negative sentiments towards the services of PT. Pos Indonesia (Persero). Researchers use social media Twitter as a medium to get data to examine the performance of the Indonesian Post. In this research, the writer intends to analyze the sentiment towards PT. POS Indonesia (Persero) as an identification material for negative and positive opinions by using the nave Bayes algorithm to determine the service performance of PT. POS Indonesia (Persero). Researchers also use CRISP-DM as a data processing method and use rapid miner applications to obtain, process and produce positive and negative classifications. Classification of data in this study took 141 tweets discussing PT. POS Indonesia (Persero) on Twitter media by using the keyword Pos Indonesia. The results of this study resulted in a positive sentiment value of 63% and a negative 37%. With the highest accuracy, the 80:20 data split method uses the Naive Bayes algorithm of 64.29%.
Penerapan Exponential Smoothing untuk Optimasi Linear Regression dalam Peramalan Perkara Lalu Lintas Ahadti Puspa Sari; Deni Mahdiana; Brury Trya Sartana; Rusdah Rusdah
KRESNA: Jurnal Riset dan Pengabdian Masyarakat Vol 3 No 2 (2023): Jurnal KRESNA November 2023
Publisher : DRPM Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/kresna.v3i2.91

Abstract

Pelanggaran lalu lintas merupakan salah satu masalah yang memicu terjadinya kecelakaan yang dapat menyebabkan adanya korban jiwa, luka ringan maupun luka berat. Sehingga pentingnya meramalkan perkara lalu lintas guna memberikan informasi kepada pemerintah dan pihak terkait mengenai kenaikan atau penurunan perkara lalu lintas yang terjadi pada bulan berikutnya, sehingga pemerintah dan pihak yang terkait dapat lebih serius dalam mengatasi kasus perkara lalu lintas di tahun berikutnya. Salah satu cara yang dapat dilakukan pengolahan data dengan menggunakan data mining. Dalam penelitian ini menggunakan peramalan atau forecasting untuk memperoleh gambaran mengenai nilai dari suatu data di masa mendatang. Metode Linear Regression mempunyai kelebihan diantaranya metode ini simple dan mudah dipahami tetapi memiliki hasil yang akurat, dan dapat memprediksi perkara lalu lintas dimasa mendatang berdasarkan nilai pelanggaran lalu lintas dimasa lampau. Maka pada penelitian ini, menggunakan algoritma Linear Regression yang dikembangkan dengan metode Exponential Smoothing guna meningkatkan kualitas data sehingga dapat meningkatkan akurasi prediksi pada Linear Regression dengan nilai Root Mean Square Error (RMSE) yang lebih baik. Kesimpulan yang didapatkan dari eksperimen yang dilakukan adalah bahwa memprediksi jumlah perkara lalu lintas menggunakan Split dataset dengan metode Linear Regression menghasilkan nilai RMSE sebesar 0.011 dan eksperimen menggunakan Split dataset dengan metode Linear Regression yang dikembangkan melalui metode Exponential Smoothing lebih akurat dengan nilai RMSE sebesar 0.002 dibanding metode Neural Network sebesar 0.003, metode Deep Learning sebesar 0.003 dan metode Support Vector Machine sebesar 0.916.
Penerapan Algoritme Backprogation Neural Network untuk Peramalan Harga Saham Alphabet Inc Andhika Arethuza Ari; Deni Mahdiana
KRESNA: Jurnal Riset dan Pengabdian Masyarakat Vol 3 No 2 (2023): Jurnal KRESNA November 2023
Publisher : DRPM Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/kresna.v3i2.94

Abstract

Saham adalah bentuk investasi yang populer dikalangan masyarakat, pada tahun 2022 Jumlah investor di pasar modal terus bertambah seiring dengan tumbuhnya kesadaran masyarakat untuk berinvestasi saham yang didukung oleh maraknya teknologi digital. Berdasarkan data Pusat Kustodian Efek Indonesia (KSEI), hingga Agustus 2022, jumlah investor di pasar modal telah menembus 9,54 juta investor. Banyak golongan investor yang berinvestasi di pasar saham dengan harapan menghasilkan keuntungan. Alphabet Inc. merupakan salah satu perusahaan teknologi terbesar di dunia yang terdaftar di bursa saham Amerika Serikat. Perusahaan ini terkenal dengan produk-produknya seperti Google, YouTube, dan Android. Sebagai perusahaan publik, harga saham Alphabet Inc. Perusahaan ini didirikan pada tahun 2015 setelah Google melakukan restrukturisasi dan mengubah nama perusahaannya menjadi Alphabet Inc. Saham Alphabet Inc. terdaftar di bursa saham Nasdaq dengan kode saham GOOGL dan merupakan salah satu saham yang paling banyak diperdagangkan di pasar saham Amerika Serikat. Penelitian ini bertujuan untuk mengembangkan model peramalan berdasarkan metode Backprogation Neural Network untuk memprediksi harga saham Alphabet Inc.. Hasil penelitian ini mendapatkan bahwa peramalan menggunakan algoritme Bacprogation Neural Network dengan split data 60 : 40 menghasilkan hasil peramalan yang baik karena menciptakan nilai RMSE yang rendah yaitu 0.462, serta. Serta dengan menggunakan algoritme backprogation menghasilkan Threshold sebesar 0.190.
PERANCANGAN APLIKASI PERMOHONAN SURAT MAHASISWA BERBASIS WEB DENGAN MENGGUNAKAN MODEL WATERFALL DAN FRAMEWORK CODEIGNITER Ikhwan, Ikhwan; Mahdiana, Deni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5639

Abstract

Surat mahasiswa adalah dokumen mahasiswa yang sangat penting dalam proses administrasi perkuliahan. Surat tersebut meliputi aktif kuliah, cuti kuliah, pindah kuliah, penelitian dan surat keterangan lulus. Saat ini pelayanan surat menyurat pada bidang akademik di Universitas Serang Raya masih dilakukan secara konvensional dan manual. Mahasiswa harus mengunjungi ruang akademik dan mengisi formulir pengajuan surat setelah itu staff akademik membuat surat yang diajukan. Untuk memaksimalkan pelayanan administrasi universitas perlu dilakukan digitalisasi pelayanan surat menyurat. Mahasiswa dapat membuat dan mengajukan permohonan surat secara online melalui aplikasi web. Untuk mengembangkan sistem web digunakan bahasa pemrograman PHP dengan framework codeigniter dan database MySQL. Framework ini digunakan untuk memudahkan dan mempercepat pembuatan aplikasi web. Metode penelitian menggunakan model waterfall yaitu metode untuk memudahkan langkah – langkah yang terstruktur agar memudahkan dalam pengembangnan sistem. Tujuan dari penelitian ini adalah memudahkan peneliti untuk menyelesaikan masalah pengajuan surat yang belum efisiensi, tersistem dan aksesibilitas. Hasil penelitian adalah aplikasi web yang dari hasil penelitian ini diharapkan dapat membantu dalam pelayanan akademik sehingga dapat memberikan kemudahan, efisiensi dan aksesibilitas yang lebih baik pada pelayanan surat.
Prediksi Potensi Keterlambatan Pembayaran Biaya Kegiatan Sekolah Menggunakan Algoritma Naïve Bayes Solehan, Solehan; Sugiarto, Sugiarto; Mahdiana, Deni; Kharmytan, Yan Baktra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7831

Abstract

Tuition fees are one of very important component in the implementation of education and school development, because tuition fees are the main requirement to be able to implement school programs that have been designed in the one school year activity plan, apart from that, school fee is also used for maintenance or development of school facilities and infrastructure. Cempaka Vocational School is a private school in Central Jakarta, region two which requires its students to pay school activity fees by paying for each activity (7 days before the activity takes place) or in installments every month (total activity costs for one year divided by twelve ). Meanwhile, based on data obtained from the school treasurer, there were 23.2% arrears in the 2020/2021 school year and 38.7% arrears in the 2021/2022 school year (The data used in this research was taken from student payment data for the 2020/2021 and 2021/2022 school years ) which has not been resolved by students, this will become a big problem for Cempaka Vocational School if a solution is not immediately found to overcome this problem. The aim of this research is to build a prediction system using the Naïve Bayes method which will produce an accurate or late classification in paying school activity fees to be used as a recommendation in policy making and finding solutions early on so that there are no delays in paying school fees by students. /i. The results of this research produced an accuracy of 70.83%, precision of 70.59% and recall of 85.71 so that it could predict delays in school activity costs according to the needs of Cempaka Vocational School.
Predicting Student On-Time Graduation Using Particle Swarm Optimization and Random Forest Algorithms Rahman, Arif; Mahdiana, Deni; Fauzi, Achmad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33577

Abstract

Higher education plays a crucial role in human resource development and national progress. A key indicator of educational quality is students' ability to graduate on time. Delays in graduation can lower the quality of higher education. Various academic and non-academic factors influence timely graduation rates. At Universitas Islam Syekh Yusuf, the trend of students graduating beyond the expected timeframe has risen over the past three years. However, the university lacks insight into the factors contributing to these delays. This research aims to identify factors causing delayed graduation using PSO and Random Forest to predict student graduation outcomes. The application of PSO reveals key factors influencing timely graduation, including study program, student active status, student leave of absence status, inactive status for semester 1, GPA1, and credit hours in semesters 1 and 2. Evaluation results show that using PSO and Random Forest to predict timely graduation achieves high accuracy (99.63%), precision (99.77%), recall (99.65%), and F1 score (99.71%).
IMPLEMENTATION OF SCRUM FRAMEWORK IN AGILE TECHNOLOGY IN GUEST RECEPTION OF GOVERNMENT OFFICES: MONITORING OF GUEST RECEPTION DASHBOARD Maulana, Hanif; Mahdiana, Deni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6084

Abstract

The presence of guests from various circles at the Prosecutor's Office is very diverse, so guest data collection is needed to monitor guests who come to the prosecutor's office. Data collection is carried out by adding several features to the dashboard according to the year, including the number  of guests  and guest  graphs of various types,  including, the average number of visit duration, the overall number of visits, the number of registered guests, the number of vehicles entering, the number of PTSP visits, the number of ticket taking, the visit graph, the guest type graph, the guest type graph, the visit type graph, the guest age graph, the guest arrival time graph, the time of arrival at the kamdal,  graph of the time of guests coming to PTSP, graph of total visits by day, graph of total visits by month, graph of the most visitors received, graph of total survey votes, and graph  of most frequently  visited guest data. The method used is development  by implementing  the Scrum Framework, one of the Agile development methodologies. The stages carried out consist of, problem identification, literature study, product backlog and sprint planning, sprint backlog, daily scrum, as well as sprint review and restrospective sprints. The duration of the work time for this project is estimated at 39 working days. The final results  obtained from  this research are in the form of details of the work and scheduling, to the prototype of the interface.
Analisis Penerapan Machine Learning, Deep Learning, dan Data Mining dalam Prediksi Penjualan di Industri Otomotif Adi Saputra, Yulian; Mahdiana, Deni
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 6 (2025): JPTI - Juni 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.826

Abstract

Persaingan bisnis yang semakin ketat di era globalisasi menuntut perusahaan, termasuk industri otomotif, untuk mampu memenuhi kebutuhan pasar secara cepat dan tepat. Salah satu strategi yang dapat diterapkan adalah melakukan prediksi penjualan kendaraan guna menunjang proses perencanaan produksi dan pengambilan keputusan strategis. Penelitian ini bertujuan untuk melakukan Systematic Literature Review (SLR) mengenai penerapan teknologi machine learning, deep learning, dan data mining dalam prediksi penjualan kendaraan. Fokus kajian ini mencakup identifikasi sebaran publikasi ilmiah dari tahun 2021 hingga 2025, pendekatan prediksi dan teknik prediksi yang digunakan, serta metode evaluasi yang diterapkan. Artikel-artikel tersebut difilter dengan kata kunci penelusuran menggunakan pendekatan PICO yang telah ditentukan, sehingga menghasilkan 19 artikel yang dikaji. Hasil yang diharapkan dari penelitian ini adalah memberikan gambaran komprehensif tentang tren penelitian di bidang ini, pendekatan-pendekatan populer seperti level data, algoritma, dan hybrid, serta mengidentifikasi metode prediksi yang paling sering digunakan seperti Exponential Smoothing dan teknik evaluasi yang umum diterapkan seperti MAPE dan MSE. Dengan temuan ini, penelitian diharapkan dapat menjadi referensi penting bagi peneliti dan pelaku industri dalam memilih metode prediksi yang tepat dan meningkatkan akurasi peramalan penjualan kendaraan.
Co-Authors A Djafar, Muhammad Agung Abdurrahman, Faris Nur Achmad Fauzi Ade Davy Wiranata Ade Setiadi Adi Saputra, Yulian Adiputra, Januar Ahadti Puspa Sari Airlambang, Dwiki Akhmad Wijaya Kusuma Amalia Khairunisa Andhika Arethuza Ari Anita Diana Arif Rahman Arifin Istighfari Zahro Atik Ariesta auddie mahlyda Bagas Wahyu Putratama Bayu Aji Susilo Brury Trya Sartana Chairul Kahfi Dahlia Mariyam Ohorella Daniel Iskandar Dedy Mirwansyah Devit Setiono Diah Ayu Lestari, Diah Ayu Diana Putri djuan narita Erly Krisnanik Fahlevi, Noval Febriansyah Ramadhan Fergina, Anggun Gita Cahyani, Annisa Putri Haderiansyah Haderiansyah Hasibuan, Tuhfatul Habibah Irgi Arifal Nulhakim janah purwanti Jejen Jaenudin Jumaryadi, Yuwan Ken Putri, Lulasnov Viola Prameswari Khafistia Hayyu Kharmytan, Yan Baktra Kraugusteeliana Kraugusteeliana Kusumawardhany, Nidya Kusumo Adi Lauw Li Hin Leonardus Adityo Toto Pratomo Maemunah Mahendrasyah, Ihjal Manarul Haikal Casandy Manda, Seftifin Ratna Maulana, Hanif Mirza Sutrisno Mohammad Aldinugroho Abdullah Muhammad Abduh Khairullah Muhammad Arifin Mutia Hasanah Nurramdhani, Helena Purwo Setyo Aji Putri, Jasmin Maula Rahmat Hidayat Ramadani, Romi Ratna Kusumawardani Ratna Kusumawardani Renaldi Setiawan Putra Rifqi Fitriadi Riskiyono, Fajar Rusdah Rusdah Rusdah Sarastuti, Elina Seftifin Ratna Manda Solehan Solehan Sri Devi Yulita Sugiarto S Supardi Supardi Syahid, Achyar Jhonathan Syifa Aryanti Tjahjanto, Tjahjanto Wiguna, Kevin Zahran, Aziz