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All Journal International Journal of Electrical and Computer Engineering Teknika Jurnal Computech & Bisnis (e-Journal) Jurnal Informatika dan Teknik Elektro Terapan Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Paradigma METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Insentif Jurnal Informatika Global Indonesian Journal of Electrical Engineering and Computer Science JIKA (Jurnal Informatika) Jurnal Inovasi Penelitian Prosiding Seminar Teknologi Kebumian dan Kelautan (SEMITAN Jurnal Responsif : Riset Sains dan Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Cakrawala Ilmiah Informatics Management, Engineering and Information System Journal Electronic, Business, Management and Technology Journal
Rizal Rachman, Rizal
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Social Sciences Transportation Other

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SISTEM MONITORING DAN KONTROL PEMBERIAN PAKAN IKAN BERBASIS IOT MENGGUNAKAN BLYNK Risman, Risman; Rachman, Rizal; Arifin, Toni
Jurnal Responsif : Riset Sains dan Informatika Vol 6 No 2 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i2.1627

Abstract

Dalam era modern, permintaan akan sistem otomatis pemberian pakan ikan berbasis IoT semakin tinggi. Penelitian ini bertujuan mengembangkan sistem monitoring dan kontrol pemberian pakan ikan berbasis NodeMCU ESP8266 dan aplikasi Blynk. Sistem ini menggunakan servo untuk pemberian pakan otomatis dan manual melalui Blynk. Uji coba menunjukkan performa baik, sistem responsif merespons perintah pengguna dari jarak jauh. Akurasi gerakan servo mencapai 100%, mengindikasikan kualitas sistem yang kuat. Penelitian menyimpulkan bahwa sistem ini dapat dioperasikan secara efisien oleh pengguna dari jarak jauh melalui Blynk. Sistem ini berpotensi mengelola pemberian pakan ikan secara optimal, mendukung pertumbuhan dan kesehatan ikan. Dalam perkembangan teknologi, sistem ini membuka peluang baru dalam menjaga keberlangsungan akuakultur dengan pendekatan yang lebih pintar dan terhubung secara digital.
PPH 21 Tax Calculation Application for Permanent Employees at The Bandung Regency Fire Service Kirani, Sandra; Sudrajat, Jajat; Rachman, Rizal; Damayanti, Resti Aulia
Electronic, Business, Management and Technology Journal Vol. 2 No. 1 (2024): Electronic, Business, Management and Technology Journal
Publisher : P3M, STIE Pasundan, Bandung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55208/ebmtj.v2i1.133

Abstract

This study aims to create a software application that can accurately compute PPh 21 (income tax) for full-time employees at the Bandung Regency Fire Department. The development process will utilize an Object-Oriented Analysis and Design (OOAD) System Development approach. The primary aim of this study is to mechanize the computation of PPh 21 in compliance with relevant tax statutes while streamlining tax and payroll administration at the organization. The development methodology comprises five primary phases: requirements analysis, system design, implementation, testing, and assessment. During the requirements analysis phase, data is gathered through interviews, observations, and literature research to comprehend the system's functional and non-functional requirements. The system design step entails developing a system architectural design that includes class, use case, and interaction diagrams to guarantee that the system fulfills the given requirements. Implementation involves creating program code based on the design and integrating system components. The testing phase encompasses unit, integration, system, and acceptance testing to verify that the system operates as intended and is error-free. The evaluation phase incorporates the participation of end users to gather input on the system's usability, efficiency, and precision and introduce enhancements prior to its complete implementation. The research findings demonstrate that using this internet-based application can enhance the effectiveness and precision of tax computations, mitigate the likelihood of data loss, and facilitate heightened job efficiency at the Bandung Regency Fire Department. This application is anticipated to favorably impact tax and payroll management at the agency while also enhancing the quality of public services through digitalization and technological innovation.
Artificial intelligence detection of refractive eye diseases using certainty factor and image processing Rachman, Rizal; Susanti, Sari; Suhendi, Hendi; Satyanegara, Adi Karawinata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1787-1797

Abstract

Refractive errors are defined as an impairment in the eye’s capacity to focus light, resulting in the formation of blurred or unfocused images. These issues arise from alterations in the shape of the cornea, the length of the eyeball, or the aging of the crystalline lens. It is anticipated that the prevalence of visual impairment will increase in conjunction with global population growth. At present, a significant number of countries have not yet accorded sufficient priority to eye health within their healthcare systems. This has resulted in insufficient awareness and reluctance to seek costly specialized care. This study proposes the development of an advanced refractive eye disease detection system with the objective of improving diagnostic accuracy, disseminating disease information, and reducing financial barriers to specialist consultation. The research employs certainty factor (CF) methods and image processing with feature extraction. The initial results demonstrate the potential for identifying specific refractive eye diseases with high certainty through the analysis of symptoms and the examination of photographs of the eye. The proposed approach provides an alternative method for diagnosing refractive eye diseases, which could enhance access to refractive eye care services and reduce the economic burden on patients.
Breast cancer identification using machine learning and hyperparameter optimization Arifin, Toni; Prasetyo Agung, Ignatius Wiseto; Junianto, Erfian; Rachman, Rizal; Wibowo, Ilham Rachmat; Agustin, Dari Dianata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1620-1630

Abstract

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.
Application Of C4.5 Model Inpatient Medical Record Information System Based on ICD-10 Diagnosis Classification : Case Study of Uni’s Dental Care Mardiansyah, Kalsi Kireina; Hamdani, Mohammad; Rachman, Rizal; Suciyono, Nanang; Sudarsono, Nono
Electronic, Business, Management and Technology Journal Vol. 2 No. 2 (2024): Electronic, Business, Management and Technology Journal
Publisher : P3M, STIE Pasundan, Bandung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55208/ebmtj.v2i2.177

Abstract

Dental medical records may consist of written documentation with comprehensive and precise details regarding patient identity, diagnosis, medical history, ICD-10 disease codes, medical procedures, and examination result documentation. Nonetheless, issues frequently arise when the documentation of medical records remains manual. This results in prolonged data searches and frequent loss or damage of files due to disorganized storage. The employed research method is descriptive. Data gathering methods are conducted through observation and interviews. The system development methodology pertains to the waterfall model. The calculation stages can be established in the system's design and development utilizing the Object-Oriented Analysis and Design (OOAD) methodology. A patient medical record information system was developed utilizing the ICD-10 diagnosis classification, accessible to physicians and administrators remotely. This web-based system was constructed using the PHP programming language, with Visual Studio Code as the code editor, XAMPP as the web server, and MySQL as the database management system. This patient medical record website is anticipated to enable administrators and physicians to input, modify, read, and search for patient data and medical records based on examination results. It will be accessible at any time and from any location. This website can forecast the potential danger of a patient's illness depending on the patient's symptoms.
PERBANDINGAN ALGORITMA APRIORI DAN FP-GROWTH PADA ANALISIS PERILAKU KONSUMEN TERHADAP PEMBELIAN DATA ELEKTRONIK Atmaja, Gunawan Bayu; Rachman, Rizal
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.4850

Abstract

Memahami perilaku konsumen dalam industri elektronik sangat penting untuk mengembangkan strategi pemasaran yang efektif. Penelitian ini membandingkan kinerja algoritma Apriori dan FP-Growth dalam menganalisis pola pembelian konsumen berdasarkan data transaksi PT Girsang yang mencakup 6968 transaksi dalam dua tahun terakhir. Data mining digunakan untuk menemukan hubungan antar produk, dengan algoritma Apriori yang bekerja dengan pendekatan kandidat itemset dan FP-Growth yang menggunakan struktur FP-Tree untuk efisiensi pemrosesan data. Hasil penelitian menunjukkan bahwa kedua algoritma menghasilkan frequent itemsets yang sama, tetapi FP-Growth lebih unggul dalam kecepatan eksekusi. Apriori membutuhkan waktu 0.0050 detik untuk menemukan frequent itemsets dan 0.0028 detik untuk menghasilkan aturan asosiasi, sementara FP-Growth hanya memerlukan 0.0025 detik dan 0.0027 detik, masing-masing. Keunggulan FP-Growth dalam efisiensi pemrosesan membuatnya lebih sesuai untuk dataset besar. Penelitian ini menyarankan penggunaan algoritma FP-Growth untuk optimasi strategi pemasaran dan manajemen inventaris pada industri elektronik. Studi lebih lanjut disarankan untuk mengeksplorasi algoritma lain seperti Eclat dan H-Mine serta integrasi dengan big data untuk meningkatkan akurasi analisis.
Deteksi Anomali Pembayaran TPD dan TKGB dengan Isolation Forest dan Evaluasi Risiko Berbasis COSO ERM Putri, Chikal Lyra Saeni; Rachman, Rizal
Jurnal Ilmiah Informatika Global Vol. 16 No. 2: August 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i2.5619

Abstract

Discrepancies in payment systems, such as overpayments, underpayments, and recording errors, have the potential to cause financial losses and reduce institutional accountability. This study aims to detect anomalies in the payment data of Lecturer Professional Allowances (TPD) and Distinguished Professor Honoraria (TKGB) at LLDIKTI Region IV using the Isolation Forest algorithm, and to evaluate the associated financial risks through the COSO ERM framework. The data analyzed were derived from historical SPTJM Online records. The results show that the algorithm successfully identified 150 anomalies in salary data and 144 in payment data, with significant deviation scores. t-SNE visualization revealed a clear separation between normal and anomalous data, while the chi square test indicated that the anomalies were systemic in nature. The COSO ERM evaluation highlighted the highest compliance in risk identification, although weaknesses were found in data integration and reporting systems. This integrative approach proves effective in detecting anomalies and strengthening financial oversight in higher education institutions.
Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Penerapan Data Mining Metode Apriori Dan FP-Tree pada Penjualan Media Edukasi (Studi Kasus : Oisha Smartkids) Junianto, Erfian; Rachman, Rizal
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 5, No 2 (2020): November 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (787.086 KB) | DOI: 10.31294/ijcit.v5i2.8308

Abstract

Selama ini Oisha Smartkids telah melayani sekian banyak transaksi pesanan produk–produk media edukasi. Setiap data transaksi tersebut disimpan di dalam suatu sistem basis data melalui aplikasi sistem informasi manajemen. Seiring meningkatnya dunia toko online maka informasi mengenai produk-produknya menjadi kebutuhan. Salah satu yang menjadi kebutuhan penting yaitu informasi mengenai penjualan dan persediaan produk media edukasi. Algoritma Apriori termasuk jenis aturan asosiasi pada data mining. Aturan yang menyatakan asosiasi antara beberapa atribut sering disebut affinity analysis atau market basket analysis. Analisis asosiasi atau  association rule mining adalah teknik data mining untuk menemukan aturan suatu kombinasi item. FP-Tree merupakan struktur penyimpanan data yang dimampatkan. FP-tree dibangun dengan memetakan setiap data transaksi ke dalam setiap lintasan tertentu dalam FP-tree. hasil analisa dan pengujian pada transaksi penjualan media edukasi menggunakan data mining dengan algoritma apriori dari 30 data produk, 12 transaksi setiap bulannya selama tahun 2019 menghasilkan nilai minimum support = 25%, nilai minimum confidence 90% dan pola kombinasi produk dan rules sebesar 100%.  Selanjutnya dilengkapi dengan algortma FP-tree menghasilkan 10 produk best seller melalui tahap filterisasi dan menemukan pola kombinasi produk. Sehingga dari 2 metode tersebut sangat penting dalam pengambilan keputusan yang berguna untuk mempersiapkan jenis stok barang apa yang diperlukan kedepanya.So far, Oisha Smartkids has served many transactions for orders for educational media products. Each transaction data is stored in a database system through a management information system application. As the world of online stores increases, information about its products becomes a necessity. One of the important needs is information about sales and inventory of educational media products. Apriori algorithm including the type of association rules in data mining. Rules that state the association between several attributes are often called affinity analysis or market basket analysis. Association analysis or association rule mining is a data mining technique for finding the rules of a combination of items. And FP-Tree is a compressed data storage structure. FP-tree is built by mapping each transaction data into each particular path in FP-tree. analysis and testing results on educational media sales transactions using data mining with a priori algorithm of 30 product data, 12 transactions per month during 2019 resulting in a minimum support value = 25%, a minimum confidence value of 90% and a combination of product and rules pattern of 100%. Furthermore, equipped with FP-tree algortma produces 10 best seller products through the filtering stage and finding patterns of product combinations. So from the 2 methods are very important in making decisions that are useful for preparing what types of goods needed in the future.
PENERAPAN DATA MINING UNTUK MEMPREDIKSI PENDAPATAN PERUSAHAAN DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING DAN TRIPLE EXPONENTIAL SMOOTHING Nur Abdurrahman, Dhimas; Rachman, Rizal
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i1.2611

Abstract

Perusahaan telah memiliki data laporan terkait pencapaian pendapatan perusahaan, tetapi informasi dari data tersebut tidak digunakan dengan semestinya. Oleh karena itu penelitian ini bertujuan untuk mengetahui penerapan data mining untuk memprediksi pendapatan perusahaan. Metode penelitian yang digunakan adalah metode DES dan TES, yang memproses sekumpulan data pendapatan yang belum diolah dan belum dikembangkan untuk menciptakan informasi baru yang bernilai dan berguna bagi perusahaan, khususnya untuk perolehan pendapatan perusahaan. Hasil penelitian menunjukan perhitungan MAPE metode DES memperoleh MAPE 98,5 sedangkan metode TES memperoleh MAPE 5,42. Dengan perolehan MAPE yang lebih kecil metode TES lebih relevan dalam penelitian ini di bandingkan dengan metode DES, karena metode TES mempunyai tren dan musiman sehingga lebih akurat dalam perhitungannya di bandingkan dengan metode DES yang hanya mempunyai tren. Dari hasil perolehan MAPE tersebut, penggunaan metode TES dengan perolehan MAPE 5,42. Berdasarkan kriteria MAPE, maka kemampuan untuk memprediksikannya adalah sangat baik.
Co-Authors Adi Suparwo Adiva Arini Puteri Agustin, Dari Dianata Ahmad, Dean Alif Ali Mulyawan Amirul Mukminin Amirul Mukminin Ani Solihat Atmaja, Gunawan Bayu Damayanti, Resti Aulia Dean Alif Ahmad Dwi Ariyandi Egi Badar Sambani, Egi Badar Erfian Junianto Erifive Pranatal Febryan, Mochamad Hendi Suhendi Hendi Suhendi Hery Wahyudi Ignatius Wiseto Prasetyo Agung Imawan. S, Pramudya Jajat sudrajat Johanes Pratama Kirani, Sandra Latif, Dadang Luh Putu Ratna Sundari Mardiansyah, Kalsi Kireina Marjito Marjito Mayya Nurbayanti Shobary Mohammad Hamdani, Mohammad Nanang Hunaifi, Nanang Nanang Suciyono, Nanang Nono Sudarsono Nur Abdurrahman, Dhimas Pramudya Imawan. S Prasetyo Agung, Ignatius Wiseto Putri, Chikal Lyra Saeni R. Dewi Sulatriningsih Risman Risman Rissa Nurfitriana Handayani Ruchiyat Firmansyah Sandi Sopian Sandra Armayanti Saputra, Raka Aditya Sari Susanti Sari Susanti, Sari Satyanegara, Adi Karawinata Toni Arifin Tubagus Faizal Achmad Fauzian Wibowo, Ilham Rachmat WIJAYA, THIAS RIZQI Wildan Wiguna Yuda Purnama Putra