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All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal on Education Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Jurnal Review Pendidikan dan Pengajaran (JRPP) INTI Nusa Mandiri Jurnal Sistem informasi dan informatika (SIMIKA) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Indonesian Community Journal Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Implementasi Peramalan Stok Parfum Pada Imshop Parfum dengan Metode Weighted Moving Average Nur’aini, Zulfiana; Santi, Nirma Ceisa; Mahmudah, Nur; Barata, Mula Agung
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.894

Abstract

Humans are social creatures who need to communicate with each other. In this regard, it is necessary to provide comfort in a conversation. To provide this comfort, you can use perfume. Imshop Parfum is a shop that sells various choices of perfume scents, but of the various types of perfumes sold, of course there are perfumes that are the most popular and rarely purchased, this is certainly a problem if the stock of the item runs out or provides too much stock. The purpose of this study is to predict the stock of goods at Imshop Parfum. The method used in this study is the Weighted Moving Average with periods 3 and 5. The forecast results from the perfume study in June 2025 were 215 for period 3, and 212.86 for period 5. MAPE is 1.98% for period 3, and the MAPE value is 2.74% for period 5. It can be concluded that period 3 is the best and most accurate result because it has the smallest MAPE value.
Algoritme Jaringan Syaraf Tiruan pada Perangkat e-Nose untuk Klasifikasi Madu Dwi Syafi'i, Ahmad; Barata, Mula Agung; Rohmah, Roihatur
Jurnal Telematika Vol. 20 No. 1 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i1.722

Abstract

Penentuan jenis madu merupakan langkah penting guna menjaga keaslian dan mutu produk. Penelitian ini mengembangkan sistem electronic nose berbasis sensor gas MQ-3 dan MQ-135 yang merekam tiga parameter volatil utama, yaitu karbon dioksida, acetone, dan alkohol. Sebanyak 541 sampel data dinormalisasi menggunakan metode min–max, kemudian dibagi dengan skema hold-out 75 persen untuk pelatihan dan 25 persen untuk pengujian. Model klasifikasi menggunakan jaringan syaraf tiruan multilayer perceptron dengan arsitektur 3–7–3, optimizer Adam, laju pembelajaran 0,001, ukuran batch 32, dan 1000 epoch. Hasil pengujian pada 135 sampel uji menunjukkan akurasi keseluruhan sebesar 88,89. Evaluasi per kelas memperlihatkan madu hutan mencapai presisi 100, recall 100, dan F1-score 100, madu budidaya memperoleh presisi 97,1, recall 70,8, dan F1-score 82,1, sedangkan madu trigona mencapai presisi 75,0, recall 97,7, dan F1-score 84,8. Temuan ini menunjukkan bahwa kombinasi e-nose dan JST mampu mengidentifikasi madu dengan tingkat akurasi tinggi, sekaligus membuka peluang penerapan metode ini sebagai sistem deteksi cepat dalam mendukung keaslian produk madu.
IMPLEMENTASI ALGORITMA MULTIPLE LINEAR REGRESSION DALAM MENGESTIMASI HASIL PANEN TANAMAN TEMBAKAU wulan, Diah nawang; Barata, Mula Agung; Sa'ida, Ita Aristia
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3662

Abstract

Penilitian ini bertujuan untuk menaksir panen tembakau petani di Desa Balongrejo menggunakan algoritma regresi linier khusus. Data yang digunakan terdiri dari empat variabel dasar: jumlah bit, jumlah pembelian, jumlah transaksi, dan jumlah jam. Analisis dilakukan secara manual dan dengan bantuan alat statistik. Hasil analisis data menunjukkan bahwa model regresi dapat menjelaskan 95,5% varians dalam data. Selain itu, uji F menunjukkan semua variabel memiliki pengaruh yang signifikan secara bersamaan, sedangkan uji t mengidentifikasi tiga variabel yang memiliki pengaruh signifikan secara terpisah.
KOMPARASI METODE SVM DAN C4.5 DENGAN BACKWARD ELIMINATION UNTUK KLASIFIKASI STRES Efendi, Ervina Putri; Barata, Mula Agung; Ardianti, Aprillia Dwi
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2130

Abstract

Kesehatan mental mahasiswa menjadi perhatian penting dalam dunia pendidikan, terutama terkait tingkat stres yang dialami selama proses akademik yang dapat berdampak pada prestasi dan kesejahteraan mereka. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Support Vector Machine (SVM) dan C4.5 dalam mengklasifikasikan tingkat stres mahasiswa guna menentukan metode yang lebih optimal. Penelitian ini menggunakan dataset dari Kaggle yang berisi 1.100 data mahasiswa dengan 20 atribut penyebab stres. Data tersebut diproses melalui tahapan normalisasi menggunakan MinMax Scaling dan seleksi fitur dengan metode Backward Elimination untuk mengoptimalkan model. Klasifikasi tingkat stres dibagi ke dalam tiga kategori: ringan, sedang, dan berat. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil analisis menunjukkan bahwa algoritma SVM memberikan akurasi tertinggi sebesar 91% dengan nilai presisi, recall, dan F1-score yang konsisten, sementara algoritma C4.5 menghasilkan akurasi 90% dengan hasil evaluasi yang serupa. Temuan ini menegaskan bahwa SVM lebih unggul dalam mengklasifikasikan tingkat stres mahasiswa dibandingkan C4.5. Kesimpulan dari penelitian ini adalah bahwa penerapan machine learning, khususnya SVM, dapat menjadi pendekatan efektif untuk deteksi dini tingkat stres mahasiswa dan berpotensi digunakan sebagai dasar pengembangan sistem pendukung keputusan dalam upaya pencegahan masalah kesehatan mental di lingkungan pendidikan.
Peramalan Penjualan Obat dengan Menggunakan Metode Single Moving Average Hidayah, Alvinatul; Barata, Mula Agung; Ardianti, Aprillia Dwi
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): Oktober 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8256

Abstract

Luas Nusa Pharmacy faces challenges in managing drug inventory due to unpredictable demand fluctuations, often leading to overstocking or shortages. This situation affects operational efficiency and customer satisfaction. Therefore, a forecasting method is needed to help predict stock requirements more accurately. Forecasting is the process of estimating future needs based on historical data analysis, aimed at supporting decision-making in inventory management. This study employs the Single Moving Average (SMA) method to forecast drug stock at Luas Nusa Pharmacy. Weekly data from 10 best-selling drugs, namely Sanmol Tab, Andalan Biru, Promag Tab, Pirocam, Voltadex, Wiros, Tolak Angin, Stanza, Kalmethasone, and Antangin, were used as the basis for calculations over the past year. The study tested three forecasting periods: 3, 4, and 6 weeks. The results indicate that the 4-week period provides the most accurate prediction with the lowest error values: MAD of 34.80986, MSE of 1797.98, and MAPE of 13.80044, achieving an accuracy rate of 86.20%. The predicted drug stock for the following week, based on the 4-week period, is 224 units. With its high accuracy, the 4-week SMA method is recommended as an effective approach to help Luas Nusa Pharmacy manage drug inventory more efficiently. The implementation of this method is expected to minimize the risk of overstocking or shortages, improve operational efficiency, and ensure optimal service to the community.
Implementation of ANN Optimization with SMOTE and Backward Elimination for PCOS Prediction Ilmiyah, Miftakhul; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22886

Abstract

by women, making it potentially fatal owing to delayed diagnosis and treatment. With the advent of current technology, machine learning and medical care may become associated with disease prediction. The purpose of the study is to predict PCOS using an Artificial Neural Network (ANN) Deep Learning algorithm combined with Synthetic Minority Oversampling Technique (SMOTE) for data balancing and backward elimination for feature selection, aiming to provide a more accurate diagnosis of PCOS with high accuracy from thoose combination. Methods: ANN algorithm structure with three hidden layers, each with a ReLU activation function of 128, 64, and 32 neurons, a Dropout layer, an output layer with a sigmoid activation function, and an Adam learning rate. Result: Using the SMOTE approach for data balance and backward elimination feature selection, the research attributes are reduced to 18. And ANN algorithm predicts PCOS disease achieve an accuracy of 92%. Novelty: This study uses an ANN algorithm model combined with the SMOTE data balancing technique and a feature selection method using backward elimination. These methods and techniques have proven to have high accuracy. The results of this study are expected to be used as a more accurate diagnosis by medical professionals in predicting PCOS disease.
Using K-NN Algorithm for Evaluating Feature Selection on High Dimensional Datasets Silfana, Fina Indri; Barata, Mula Agung
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.40866

Abstract

Data mining is the process of using statistics, mathematics, artificial intelligence and machine learning to identify problems that exist in data so as to produce useful information. Based on its function, data mining is grouped into description, estimation, classification, clustering, and association. K-NN is one of the best data mining methods and is widely used in research. K-NN algorithm was introduced by Fix and Hodges in 1951. K-NN algorithm is a simple algorithm and is often used to cluster supervised data. Feature selection attribute selection is a data mining technique used in the pre-processing stage. This technique works by reducing complex attributes that will be managed at the processing and analysis stage. In this study, the most effective feature selection to improve the accuracy of the K-NN algorithm by increasing accuracy by 95.12% on the breast cancer dataset and 88.75% on the prostate cancer dataset.
ANALISIS STRATEGI GREEN MARKETING, STORE ATMOSPHERE, DAN BRAND AMBASADOR TERHADAP MINAT BELI PELANGGAN THE BODY SHOP DI GRAND CITY SURABAYA Irnawati, Dwi; Barata, Mula Agung
Jurnal Review Pendidikan dan Pengajaran Vol. 7 No. 3 (2024): Vol. 7 No. 3 (2024): Volume 7 No 3 Tahun 2024 (Special Issue)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i3.31487

Abstract

Penelitian ini memiliki tujuan untuk menganalisis strategi Green Marketing, Store Atmosphere, dan Brand Ambassador Terhadap Minat Beli pelanggan The Body Shop di Grand City Surabaya. Penggunaan metode dalam penelitian ini adalah metode kuantitatif dan hasil penelitian didasarkan pada jawaban responden dengan menggunakan skala Likert 1-5. Penelitian ini menggunakan populasi  pelanggan The Body Shop di Grand City Surabaya, dan sampel yang digunakan dalam penelitian ini berjumlah 150 responden. A Store Atmosphere Store Atmosphere nalisis data yang digunakan dalam penelitian ini adalah Uji Regresi Linier Berganda, Uji f, Uji t dan Koefisien determinasi (R2). Hasil penelitian menunjukkan bahwa secara simultan (uji f) menunjukkan terdapat pengaruh yang signifikan antara variabel Green Marketing (X1), Store Atmosphere (X2) dan Brand Ambassador (X3) terhadap Minat Beli Pelanggan (Y). secara parsial (Uji t) Brand Ambassador tidak berpengaruh signifikan terhadap Minat Beli pelanggan dan Store Atmosphere juga tidak berpengaruh signifikan terhadap Minat Beli pelanggan, sedangkan Green Marketing berpengaruh signifikan terhadap Minat Beli pelanggan.
Perbandingan Akurasi Algoritma Naive Bayes dan Algoritma Decision Tree dalam Pengklasifikasian Penyakit Kanker Payudara Munir, Ach Sirojul; Saputra, Agus Bima; Aziz, Abdul; Barata, Mula Agung
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3578

Abstract

Cancer is one of the deadliest diseases in the world with a high increase in the number of cases every year Cancer disease with significant growth in cases, is a serious global challenge. The main focus of this research is breast cancer in Indonesia. Using a data mining approach, this study compares two main classification algorithms, namely Naive Bayes and Decision Tree, to identify breast cancer. Naive Bayes is a simple probabilistic approach, calculating probabilities assuming attribute independence. Decision Tree, as a popular algorithm, represents decision rules in the form of a tree. Through comparison with previous research on algorithms in other contexts, this study aims to find the algorithm with the highest accuracy in breast cancer classification. With the final result, the decision tree has a higher accuracy of 92.04% and naïve Bayes has an accuracy of 91.15%.This result proves that the decision tree is superior in the classification of breast cancer disease compared to naïve Bayes. The results of the study are expected to make an important contribution to the development of effective approaches for the diagnosis and treatment of breast cancer.
Co-Authors Abdul Aziz Amalia, Salsabila Dani Andiyani, Putri Aprillia Dwi Ardianti Deni Reskianto Deni Denny Nurdiansyah Dina, Intan Rachma Dwi Irnawati Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Edi Noersasongko Efendi, Ervina Putri Fina Indri Silfana Hidayah, Alvinatul Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa'ida Jauhar Vikri, Muhammad Laili, Elok Fathiyatul Levia, Zachdyna Aurelya Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Munir, Ach Sirojul Muzakka, Moch. Arifuddin Nasirudin, M. Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur’aini, Zulfiana Panigoro, Buyung Pelangi Eka Yuwita Pratiwi, Fannisa Salsabila Purwanto Purwanto Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Sahri Sahri Sahri Santi, Nirma Ceisa Saputra, Agus Bima Silfana, Fina Indri Sinta Ningrum Usman Nurhasan Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi