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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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Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose Mula Agung Barata; Edi Noersasongko; Purwanto; Moch Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4687

Abstract

Tea is one of the plantation products within the Ministry of Agriculture of the Republic of Indonesia, which plays an essential role as a mainstay commodity that boosts the Indonesian economy. Each type of tea has different properties, and the aroma of each type of tea can measure the quality of the tea. The human sense of smell is still very limited in classifying pure types of tea. Therefore, a device is needed to help measure the aroma of tea from an electronic nose. The devices attached to several gas sensors help humans take data from the smell of pure tea and calculate the value of each type of tea to test datasets with data mining algorithms. This study uses the C4.5 algorithm as a classification method with advantages over noise data, missing values, and handling variables with discrete and continuous types. Meanwhile, Chi-square is used to perform attribute severing in the data preprocessing process to increase the accuracy of dataset testing. Testing a pure tea dataset with four whole attributes, namely CO2, CO, H2, and CH4, using the C4.5 algorithm resulted in an accuracy of 93.65% and an increase in the accuracy performance of the C4.5 algorithm by 94.27% with dataset testing using Chi-Square feature selection with the two highest value attributes.
Persepsi Mahasiswa Teknik Informatika Terhadap Perkuliahan Daring Sebagai Sarana Pembelajaran Selama Pandemi Covid-19 Ita Aristia Sa’ida; Sahri Sahri; Mula Agung Barata
Journal on Education Vol 6 No 1 (2023): Journal On Education: Volume 6 Nomor 1 Tahun 2023
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v6i1.3113

Abstract

With the COVID-19 pandemic, universities in Bojonegoro Regency, especially Nahdlatul Ulama Sunan Giri University, are conducting online lectures. In the implementation of learning, it is different from conventional lectures so that the use of learning models determines the effectiveness of online lectures during the COVID-19 period. This study examines the perceptions of Informatics Engineering students' learning models through approaches to the use of learning media in online lectures, learning communication models, learning styles, and the effectiveness of online lectures. This research is specifically for Informatics Engineering students whose learning is not only limited to delivering material but needs to be carried out in practicum both in the laboratory and in the field. Along with the online learning process, some material that should have been carried out by research had to not be carried out. Given these obstacles, student perceptions regarding the effectiveness of online lectures that lead to learning models with learning media approaches, communication models, learning styles among Informatics Engineering students in Bojonegoro Regency determine the success of this learning process. With this, researchers examine the perceptions of Informatics Engineering students towards online lectures during the COVID-19 pandemic. This research was conducted to determine student perceptions of online learning. This research uses Descriptive Analysis, namely to examine the perceptions of Informatics Engineering students regarding online learning models during the COVID-19 pandemic. Which online learning model can be known through several approaches, such as online learning media, learning communication models, learning styles and the effectiveness of online learning. Data obtained from observations in the form of giving online questionnaires. The samples involved were Informatics Engineering students at Nahdlatul Ulama University Sunan Giri Bojonegoro as respondents in this study. The output targeted in this study is Sinta 4 indexed National Journal Publications with research TKT 3.
IMPLEMENTASI RASPBERRY PI UNTUK RANCANG BANGUN SISTEM KEAMANAN PINTU RUANG SERVER DENGAN PENGENALAN WAJAH MENGGUNAKAN METODE TRIANGLE FACE Indra Dharma Wijaya; Usman Nurhasan; Mula Agung Barata
Jurnal Informatika Polinema Vol. 4 No. 1 (2017): Vol 4 No 1 (2017)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v4i1.138

Abstract

Minicomputer raspberry pi merupakan sebuah alat yang praktis dalam segi dimensi dan memiliki fungsi yang kompleks untuk berbagai kebutuhan fungsi yang akan digunakan oleh manusia sebagai microcontroller, server sampai dengan pengolahan citra digital. Penelitian dilakukan bertujuan untuk membantu memenuhi kebutuhan sistem keamanan ruang server yang mudah untuk diaplikasikan dan murah dalam segi biaya pembuatan dan perawatan serta berteknologi, mengingat pentingnya keamanan data dan informasi yang tersimpan dalam server sehingga perlu pengamanan dalam mengakses ruang server pada suatu perusahaan. Dengan memanfaatkan minicomputer raspberry pi sebagai pemroses dan usb webcam sebagai alat pendeteksi wajah yang kemudian akan diproses oleh raspberry pi dengan menggunakan OpenCV untuk menentukan wajah manusia atau bukan, lalu wajah tersebut akan masuk pada proses pengenalan wajah dengan metode triangle face yang memanfaatkan perhitungan jarak antar fitur wajah seperti mata, hidung dan mulut. Setelah wajah dikenali maka raspberry pi akan melakukan perintah pada servo untuk membuka pintu ruang agar dapat diakses oleh admin server pada suatu perusahaan. Berdasarkan pengujian sistem yang telah dilakukan, ternyata sistem pengenalan wajah menggunakan metode Triangle Face ini memiliki tingkat keakuratan 75%, kesalahan posistif 25% dan kesalahan negatif 0% sehingga dapat disimpulkan bahwa sistem ini cukup aman untuk diaplikasikan dalam sistem keamanan pintu ruang server.
FORECASTING METODE SINGLE EXPONENTIAL SMOOTHING DALAM MERAMALKAN PENJUALAN BARANG Deni Reskianto Deni; Mula Agung Barata; Sahri
Jurnal Informatika Polinema Vol. 9 No. 4 (2023): Vol. 9 No. 4 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i4.1405

Abstract

Toko persediaan barang merupakan salah satu usaha yang bergerak di persediaan barang rumah tangga dan perkantoran yang ada di bojonegoro. Saat ini permasalahan yang terjadi pada perusahaan tersebut peramalan penjualan barang masih dilakukan secara manual. Pada umunya pula perusahaan hanya menyediakan barang sesuai kebutuhan saja dan barang yang lebih digudang sebagai penyimpanan stock. Hal ini menjadikan perusahaan tidak bisa memenuhi kebutuhan konsumen secara mendadak, kasus ini sangat memengaruhi dengan barang yang ada dijual pada periode berikutnya. Adapun tujuan dari penelitian ini adalah meramalkan penjualan barang. Dalam melakukan peramalan tersebut, data penjualan yang digunakan bulan januari 2019 sampai bulan januari 2023. Salah satu metode yang dapat digunakan untuk melakukan peramalan adalah single exponential smoothing. Single exponential smoothing adalah metode yang memfokuskan mencari nilai stabilitas yang mengambil data yang sudah ada untuk diberi fungsi exponential. Dalam menggunakan metode ini harus memberikan parameter alpha. Untuk mencari nilai akurasi terbaik pada metode tersebut, metode MAPE digunakan untuk mengukur nilai error. MAPE ini nantinya akan berguna untuk menentukan jumlah barang yang akan dijual pada periode berikunya. Berdasarkan hasil kesimpulan yang dapat diambil pada penelitian ini adalah metode ini dapat diterapkan dengan baik. Hasil dari perhitungan metode single exponential smoothing dalam meramalkan penjualan barang untuk periode berikutnya pada penjualan kasur nilai alpha 0,3 sebesar 23,65 dengan MAD 3,18 MSE 18,97 MAPE 14,68%, lemari nilai alpha 0,2 sebesar 18,35 dengan MAD 2,90 MSE 12,35 MAPE 16,60%, meja nilai alpha 0,3 sebesar 25,80 dengan MAD 3,04 MSE 14,20 MAPE 17,44%, Kursi nilai.
Sistem Otomatisasi Hidroponik Budidaya Sayuran sebagai Upaya Pemberdayaan Mandiri Santri Pondok Pesantren Pacul Bojonegoro Roihatur Rohmah; Muhammad Jauhar Vikri; Mula Agung Barata; Zakki Alawi; Moh. Muhajir; Vita Dwi Rahmawati; Rheyna Anggri Setyani
I-Com: Indonesian Community Journal Vol 4 No 2 (2024): I-Com: Indonesian Community Journal (Juni 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i2.4316

Abstract

Vegetables are a source of vitamins and fiber which are very beneficial for student growth. Vegetables can improve human brain performance. Students or santri who are in Islamic boarding schools generally consume minimal vegetables regularly. One of the Islamic boarding schools in the city of Bojonegoro. Community service activities in the form of hydroponic system training are needed by students with the aim of increasing students' knowledge of vegetable cultivation using hydroponics as an effort to empower students to independently consume vegetables. In the training activities that have been carried out, an automatic hydroponic system has been successfully created that uses a water level sensor in a DFT (Deep Flow Technique) pipe for irrigation. Service is carried out using the Rapid Rural Appraisal method and participatory learning and action. The results of the activities in this training were an increase in students' knowledge about hydroponics from 30% to 72%. Apart from that, there is also 1 hydroponic system dedicated to the Al Falah Islamic boarding school from the service team as a means of applying vegetable cultivation to meet the students' vegetable consumption needs.
ANALISIS PENERAPAN PROGRAM REWARD KEPADA CUSTOMER MENGGUNAKAN METODE CLUSTERING Novitasari, Dwi Tiyas; Barata, Mula Agung; Rochmatin, Novia Nur; Muzakka, Moch. Arifuddin; Andiyani, Putri
JURNAL BISNIS KOLEGA Vol. 10 No. 1 (2024): Juni
Publisher : STIE-PMCI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57249/jbk.v10i1.140

Abstract

This research aims to analyze consumers to provide reward programs based on their satisfaction and loyalty in shopping centers [Malls]. The research was carried out by collecting data, then analyzing the results using relevant clustering methods. Regression analysis shows a strong relationship between these variables, and this research provides strong evidence for shopping centers and similar companies, to use this method to increase their customer satisfaction. These results can help companies design reward strategies that are more effective and in line with consumer preferences. This research contributes to the literature on customer management and marketing strategy by investigating their role in increasing satisfaction and loyalty. The practical implications of this research can help companies to maximize the benefits of their investment in rewards programs, by ensuring that the programs are not only attractive to customers but also effective in creating long-term loyalty.
Pelatihan Re-Branding Produk Biopest Pada Kelompok Tani Kecamatan Ponggok Kabupaten Blitar Irnawati, Dwi; Anggapratama, Reza; Barata, Mula Agung
Darmabakti : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 5 No 01 (2024): Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Lembaga Peneliian dan Pengabdian Masyarakat (LPPM) Universitas Islam Madura (UIM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/darmabakti.2024.5.01.22-26

Abstract

Kelompok tani kecamatan ponggok kabupaten blitar mempunyai usaha pembuatan Pestisida Alami yang terbuat dari Batok kelapa dan akan dipasarkan ke masyarakat luas, tetapi seluruh anggota kelompok tani tidak memiliki kemampuan untuk mem-branding dan mengemas produk pestisida alami tersebut agar menarik konsumen oleh karena itu kemasan yang diberikan saat ini hanya biasa saja tanpa merek. Tujuan kegiatan ini yaitu untuk membantu kelompok tani kecamatan ponggok kabupaten blitar dalam pemberian brand dan mengemas produk pestisida alami agar menarik konsumen yang mempunyai daya jual tinggi. Metode pelaksanaan kegiatan ini melakukan soslialisasi, pelatihan, dan pendampingan serta yang terakhir ada evaluasi . Hasil kegiatan ini kelompok tani dapat membuat merek dan mengemas produk pestisida alami yang saat ini disebut BIOPEST lebih menarik minat konsumen. Peningkatan kemampuan kelompok tani dalam pembuatan merek dan pengemasan produk biopest yaitu 55%, yang awalnya hanya 20% saja yang mengetahui supaya produk menjadi menarik dan setelah adanya pelatihan dan pendampingan sudah meningkat menjadi 75%.
Using K-NN Algorithm for Evaluating Feature Selection on High Dimensional Datasets Fina Indri Silfana; Mula Agung Barata
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.
IMPLEMENTASI METODE SMOTE DAN RANDOM OVER-SAMPLING PADA ALGORITMA MACHINE LEARNING UNTUK PREDIKSI CUSTOMER CHURN DI SEKTOR PERBANKAN Pratiwi, Fannisa Salsabila; Barata, Mula Agung; Ardianti, Aprillia Dwi
Jurnal Sistem Informasi dan Informatika (Simika) Vol 8 No 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3678

Abstract

The ability to anticipate unsubscribed customers is a challenge in the competitive banking industry, where it is more efficient to retain customers than to attract new ones. The purpose of this study is to improve the effectiveness of churn prediction by overcoming data imbalances using SMOTE (Synthetic Minority Oversampling Technique) and Random Over-sampling. The data set used consists of 10. 000 bank customer data, with 12 important attributes, including churn indicators as targets. The machine learning algorithms used are Random Forest and Neive Bayes, evaluated based on accuracy, precision, recall, and F1 scores. The results of the experiment showed that the highest accuracy of 87.13% could be achieved with the Random Forest algorithm without using the oversampling method, but its effectiveness in detecting churn customers was slightly limited. The use of SMOTE and Random Over-sampling methods has improved the model's performance in identifying churn patterns, although it has led to a decrease in accuracy to 86.20% for Random Over-sampling and 81.47% for SMOTE. Nevertheless, the Neive Bayes algorithm showed the best accuracy rate of 79.20% without oversampling, although it was still slightly lacking in optimal churn handling. The study underscores the importance of using oversampling methods to improve prediction balance in minority classes, which is often overlooked in conventional models. It is hoped that the results of this research can be used as a guide in improving strategies to maintain customer trust that are more up-to-date and efficient.
KOMPARASI ALGORITMA DECISION TREE DAN SUPPORT VECTOR MACHINE (SVM) DALAM KLASIFIKASI SERANGAN JANTUNG Laili, Elok Fathiyatul; Alawi, Zakki; Rohmah, Roihatur; Barata, Mula Agung
Jurnal Sistem Informasi dan Informatika (Simika) Vol 8 No 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3683

Abstract

The heart is one of the most important organs in the human body. According to the WHO, heart attacks are the most common cause of sudden death worldwide, with more than 17.8 million people dying from heart attacks. A heart attack occurs when blood flow to the coronary arteries stops, depriving the heart muscle of oxygen, and causing a heart attack. Detecting a heart attack is very difficult due to the various symptoms. The purpose of this research is to compare the performance of the accuracy values of two algorithms, namely Decision Tree and Support Vector Machine (SVM) in classifying heart attacks. The results of this study show that the Decision Tree algorithm achieves the highest accuracy results compared to the SVM algorithm. The accuracy of the Decision Tree algorithm using a 60:40 ratio data splitting is 98.11% with a negative precision of 98.01% and positive of 98.17% and a negative recall of 97.04% and positive of 98.77%. Meanwhile, the SVM algorithm using data splitting with the same ratio produces an accuracy value of 92.80% with a negative precision of 90.24% and a positive of 94.43% and a negative recall of 91.13% and a positive of 93.85%.
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