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All Journal Syntax Jurnal Informatika Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Technomedia Journal Aptisi Transactions on Management CCIT (Creative Communication and Innovative Technology) Journal SENSITEK ADI Journal on Recent Innovation (AJRI) Journal of Innovation and Future Technology (IFTECH) ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Journal of Applied Data Sciences International Journal of Cyber and IT Service Management (IJCITSM) Jurnal Maklumatika Journal Collabits

Padeli Padeli

Universitas Raharja

Author-ID : 2755141

Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science

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An Extensive Exploration into the Multifaceted Sentiments Expressed by Users of the myIM3 Mobile Application, Unveiling Complex Emotional Landscapes and Insights Hayadi, B Herawan; Henderi, Henderi; Budiarto, Mukti; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik; Swastika, Rulin; Arifin, Rita Wahyu
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.187

This study investigates user sentiment towards the myIM3 application, an application used for telecommunication service management in Indonesia. Using text analysis and machine learning methods, we analyzed user reviews to identify dominant sentiment patterns and evaluate different classification models. Word cloud analysis, sentiment distribution, and donut plots were utilized to gain deeper insights into user preferences and issues. Results indicate that the majority of user reviews are neutral (52.2%), with 37% positive reviews and 33.4% negative reviews. Users consistently pay attention to aspects such as internet connection (Neutral: 92%, Positive: 95%, Negative: 87%) and pricing (Neutral: 92%, Positive: 92%, Negative: 93%) in their reviews. Evaluation of classification models like Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest shows that the SVM model performs the best with an accuracy of 93%, high precision (Negative: 93%, Neutral: 92%, Positive: 95%), recall (Negative: 93%, Neutral: 95%, Positive: 91%), and F1-score (Negative: 93%, Neutral: 94%, Positive: 93%). These findings can serve as a basis for service improvement and better product development in the future, while also affirming the capability of text analysis and machine learning techniques in providing valuable insights for telecommunication service providers.

Unsupervised Learning Methods for Topic Extraction and Modeling in Large-scale Text Corpora using LSA and LDA Henderi, Henderi; Hayadi, B Herawan; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.102

This research compares unsupervised learning methods in topic extraction and modeling in large-scale text corpora. The methods used are Singular Value Decomposition (SVD) and Latent Dirichlet Allocation (LDA). SVD is used to extract important features through term-document matrix decomposition, while LDA identifies hidden topics based on the probability distribution of words. The research involves data collection, data exploratory analysis (EDA), topic extraction using SVD, data preprocessing, and topic extraction using LDA. The data used were large-scale text corpora. Data explorative analysis was conducted to understand the characteristics and structure of text corpora before topic extraction was performed. SVD and LDA were used to identify the main topics in the text corpora. The results showed that SVD and LDA were successful in topic extraction and modeling of large-scale text corpora. SVD reveals cohesive patterns and thematically related topics. LDA identifies hidden topics based on the probability distribution of words. These findings have important implications in text processing and analysis. The resulting topic representations can be used for information mining, document categorization, and more in-depth text analysis. The use of SVD and LDA in topic extraction and modeling of large-scale text corpora provides valuable insights in text analysis. However, this research has limitations. The success of the methods depends on the quality and representativeness of the text corpora. Topic interpretation still requires further understanding and analysis. Future research can develop methods and techniques to improve the accuracy and efficiency of topic extraction and text corpora modeling.

Model Deteksi Penyimpangan Keuangan Medis Menggunakan Gradient Boosted Tree (GBT ) Pada Rumah Sakit ABC Martono, Aris; Padeli, Padeli
Journal Sensi: Strategic of Education in Information System Vol 10 No 1 (2024): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v10i1.3115

Tujuan penelitian ini yaitu untuk mengetahui penyimpangan keuangan yang terjadi di lingkungan Rumah Sakit. Penyimpangan transaksi keuangan ini melibatkan aktivitas dokter, pembuatan resep dan apotik atau farmasi serta bagian keuangan Rumah Sakit. Setiap dokter yang mengeluarkan resep untuk pengobatan pasien, diharapkan pasien membeli obat di apotik Rumah Sakit itu sendiri sehingga transaksi keuangannya menjadi pemasukan bagi Rumah Sakit. Namun sebaliknya, hal ini bisa mempersulit mengetahui pemasukan kas yang diperoleh dari setiap dokter terkait resep yang dikeluarkan. Oleh karenanya penelitian ini dilakukan dengan membuat model untuk mengetahui penyimpangannya. Untuk mendapatkan model yang terbaik dilakukan evaluasi model terhadap algoritma Gradient Boosted Tree(GBT) dan Random Forest(RF). Hasilnya adalah AUC (Area Under the Curve) model GBT = 0.976 dan AUC model RF = 0.964 yang menunjukkan bahwa algoritma GBT pilihan terbaik untuk pemrosesan penyimpangan transaksi keuangan dataset medis di Rumah Sakit ABC.

Credit Risk Prediction Model Using Support Vector Machine with Parameter Optimization in Banks Martono, Aris; Padeli, Padeli; Suhaepi, Muhamad Iip; Santoso, Sugeng; Sunandar, Endang
Journal Sensi: Strategic of Education in Information System Vol 10 No 2 (2024): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v10i2.3463

Abstract This research aims to determine the Support Vector Machine (SVM) model with Parameter Optimization in predicting loan worthiness to avoid the risk of bad credit at the Bank. Every bank tries to market financial loan products with very strict requirements. One of the requirements is that the company's financial reports must be healthy if it borrows money from a bank to develop the company's business. In the credit analysis process, there are 19 financial factors that must be measured from dozens or even hundreds of companies proposing financial loans, making it difficult for credit analysts to make decisions about whether these companies are worthy of borrowing or not. Therefore, this research was carried out by comparing the two models, namely SVM with parameter optimization and SVM with parameter optimization and Particle Swarm Optimization (PSO) to select the best model. The research results show that the Area Under Curve (AUC) criteria with validation number of folds (nof) = 10 and nof = 5 are 98.80% and 98.80%, meaning good and stable in the SVM model with parameter optimization. Meanwhile, the SVM model with parameter optimization and PSO has better AUC for validation nof=5 (99%) but for AUC with validation nof=10 (98.30%) it is less good.

Grup Google Hangouts Rinfo Sebagai Media Diskusi Kelas Pada Perguruan Tinggi Raharja Padeli, Padeli; Febriyanto, Erick; Hartanto, Hartanto
Technomedia Journal Vol 3 No 1 Agustus (2018): Technomedia Journal
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Teknologi informasi yang berkembang pesat terus melahirkan inovasi yang mempermudah manusia dalam melakukan komunikasi dan mendapatkan informasi. Dengan berkembangnya suatu teknologi kita dapat berinteraksi dengan satu sama lain tanpa ada batasan tempat dan waktu. Hal ini dapat terjadi karena adanya suatu wadah (forum) komunitas yang bisa dijadikan media untuk berdiskusi secara online. Pada Perguruan Tinggi Raharja, setiap mahasiswa diberikan fasilitas berupa email resmi yaitu Rinfo yang digunakan sebagai media komunikasi. Namun saat ini belum tersedia wadah untuk berdiskusi antara mahasiswa dan dosen, sehingga proses diskusi pembelajaran belum efektif dan efisien karena masih dilakukan secara tatap muka. Oleh karena itu, diperlukannya pemanfaatan RinfoApps yang dapat digunakan sebagai wadah diskusi pembelajaran online. Hangouts merupakan salah satu GoogleApps yang dapat dimanfaatkan sebagai wadah formal untuk proses diskusi pembelajaran online. Dengan membuat Grup Google Hangouts di setiap kelas, mahasiswa dan dosen dapat berdiskusi di luar jam perkuliahan, dosen dapat sharing mengenai bahan materi pembelajaran. Dalam penelitian ini, ditemukan 3 (tiga) permasalahan pada keadaan saat ini. Lalu dengan didukung 2 (dua) metode penelitian yaitu metode observasi dan studi pustaka. Hasil akhir yang dicapai dari penelitian ini yaitu terbentuknya media diskusi yang dapat di akses dimana dan kapan saja sehingga proses diskusi antara mahasiswa dan dosen menjadi lebih efektif dan efisien.

Perancangan Sistem Informasi Penilaian Siswa Berbasis Web Pada SMK Al-Husna Kota Tangerang Padeli, Padeli; Ramadhan, Gilang Kartika Hanum; Aprilyani, Ulfa Tiana
Technomedia Journal Vol 4 No 2 Februari (2020): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Rapid technological developments in the current era of globalization have provided many benefits in progress in various aspects. Besides being used to process data and store computer data, it can also be used to support the application and utilization of information and communication technology. At this time in the Tangerang Al-Husna Vocational School the processing or scoring system of student grades has been running semi-computerized but is still limited by using Microsoft Excel. This will result if the student value data occurs unintentional changes will affect other data as well and can cause loss or problems in the student's assessment recorded and stored in a conventional way. The purpose of this research is to change the student assessment system which is currently running semi-computerized into a system that is fully computerized, namely a web-based system and can be accessed online so that the value processing of students does not take a long time and is expected to run effectively and efficiently. The method used to analyze the problems in the research is by using the SWOT method (Strength, Weakness, Opportunities, Threats) while for the system design method using UML (Unified Modeling Language) and for programming languages using PHP and MYSQL in making the database. With the implementation of this website-based system, it is expected to facilitate the student assessment process that can be done quickly and accurately. Keywords: System, Assessment, Students

Employee Attendance Optimization Using QR Code Model with Reed Solomon Error Correction for Data Security and Accuracy Martono, Aris; Padeli, Padeli; Suhaepi, Muhamad Iip
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3762

This research aims to determine the process of creating a quick response code (QR code) model with Reed Solomon error correction for employee attendance at the Company. Fingerprint attendance systems, even though they are more sophisticated, still have disadvantages, such as difficulties in use in unhygienic environments, as well as high costs for installing the device. Apart from that, traditional attendance is also less flexible in managing employees who work in the field or employees who do not work in the main office. Companies that have many branches or employees who work outside the main office often have difficulty monitoring absenteeism effectively and accurately. The mechanism of this QR code model is carried out through several steps, namely: coding QR codes based on employee ID numbers, grouping encoder data every 8 bits, converting encoder data to binary format, error correction using the Reed Solomon algorithm, creating error correction codes (EC). ) in polynomial form, calculating error correction data based on the correspondence and index of integer numbers in the Galois Field (GF), calculating the function f′(x) through an iterative division process until completion, determining the remainder of the division in the form of R(x), as well as merging encoder data with error correction code as result end. With this mechanism, the QR code-based attendance system is able to maintain data security and accuracy while minimizing the occurrence of anomalies during the work attendance process.

Performance Evaluation of ARIMA and LSTM Models with Product Inventory Demand in Production Companies Martono, Aris; Padeli, Padeli; Suhaepi, Muhamad Iip; Tia Wulandari, Anur Rahmah
Journal Sensi: Strategic of Education in Information System Vol 11 No 2 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i2.4068

This study aims to evaluate and compare the performance of two time series forecasting approaches: the classical statistical ARIMA model and the deep learning-based LSTM model, in the context of forecasting product inventory demand in a production company. The data used consists of historical daily demand records, totaling 100 and 200 records, which were analyzed to identify linear and non linear patterns. The ARIMA model was selected for its reliability in modeling stationary and seasonal data, while the LSTM model was utilized to capture complex temporal patterns through its layered neural network architecture. The test results using the MSE and RMSE metrics show that in both datasets, the ARIMA model has better prediction performance (100 records, RMSE=45.61% and 200 records, RMSE=44.72%) compared to LSTM, namely 100 records, RMSE=45.93% and 200 records, RMSE=49.54%. Although LSTM excels in handling non-linear dynamics, ARIMA outperformed it on data with linear. This study highlights the importance of selecting forecasting models based on data characteristics and suggests opportunities for future exploration of hybrid models. The theoretical and empirical foundations of this research are supported by the works of Hyndman & Athanasopoulos (2018), Hochreiter & Schmidhuber (1997), and Makridakis et al. (2018), which provide critical insight into predictive modeling for time series analysis.

Alignment of Science and Technology With Islamic Principles Using Quantum Theory Alwiyah, Alwiyah; Husin, Syarief Nur; Padeli, Padeli; Anggaraeni, Mey; Sulistiawati, Sulistiawati
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 1 No. 1 (2021): April
Publisher : International Institute for Advanced Science & Technology (IIAST)

The wave-particle duality stranges and it is in dire need. This theory is put forward method based on the Koran and complemented by rational philosophical arguments. Explaining relevant Quranic verses, as well as the one-to-one relationship between the concept of pairing and the interviewee's principle, will help explain of the electron in detail. Shows that electrons all of which reflect the behavior of the wave-particle duality observed in experiments. Although physicists consider a magnet and the existence of a magnetic field caused by the rotation of electrons, a new theory speculates that there has also been a permanent magnetic field recently. In addition, the choice of gate charge and permanent magnets can be selected as potential energy which is also considered as possible which has been observed to exist but has not been well described. Equations have been derived electrons. In this respect, Islamic science and technology seems to have demonstrated the importance of exploring the mysterious quantum world.

Sustainable Digital Business Model Innovation through Learning Factory and AI Azizah, Nur; Supriati, Ruli; Padeli, Padeli; Mulyati, Mulyati; Apriani, Desy; Fae, Nahlie
Technomedia Journal Vol 10 No 2 (2025): October
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ygn11n27

The digital era compels organizations to continuously innovate in creating sustainable digital business models that integrate technology, human resources, and environmental sustainability. Within this transformation, the Learning Factory emerges as an innovative concept that connects theoretical learning with practical application, enabling students and industry practitioners to co-create solutions through real-world, technology-based projects. Simultaneously, Artificial Intelligence (AI) enhances analytical, predictive, and adaptive capabilities, driving efficiency, innovation, and data-driven decision-making across organizational processes. This study aims to explore the synergy between LearningFactory and AI as a strategic driver of innovation in competitive and sustainable digital business models aligned with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation, and Infrastructure). Through a qualitative methodology combining literature review and case study analysis of technology-based organizations and higher education institutions implementing the Learning Factory framework, the research identifies how AI integration strengthens learning outcomes, accelerates digital transformation, and promotes sustainability-driven innovation. The findings reveal that this synergy fostersadaptability, enhances human resource competencies, and generates economic,social, and environmental value. Furthermore, it encourages universities and industries to co-develop agile ecosystems that nurture startupreneurship, continuous learning, and inclusive innovation. Ultimately, this study provides strategic recommendations for designing adaptive, competitive, and sustainable digital business models that empower human potential while advancing organizational resilience in the era of rapid technological disruption.

Co-Authors Abas Sunarya Agus Priyatna Aji Kuspriambodo Alfian, Rifky Alvega Awanda Alwiyah Alwiyah Alwiyah Alwiyah Amal Awallya Andini Ayu Distri Anggaraeni, Mey Aprilyani, Ulfa Tiana Arifin, Rita Wahyu Aris Martono Aris Martono Astriyani, Erna Awallya, Amal B. Herawan Hayadi Bayu Pramono Bonari Simanjuntak Budiarto, Mukti Christien Setiya Kesumawati Danang Suprayogi Danang Suprayogi Debora, Siska Desy Apriani Didik Setiyadi Dina Fitria Murad Distri, Andini Ayu Dwi Oktavionita, Siti Dian Cahya Eduard Hotman Purba Erick Febriyanto Euis Sitinur Aisyah Fadli Fadillah Rahman Fae, Nahlie Fitria Supyaningsih Gustina Gustina Handayani, Revi Sajidah Sri Handayni, Revi Sajidah Sri hartanto hartanto Henderi . Husin, Syarief Nur Ilamsyah Ilamsyah, Ilamsyah Indri Handayani Irma Ayu Rodatin, Irma Ayu Kamil, Rizki Maulana Kesumawati, Christien Setiya Kuspriambodo, Aji Latifah, Della Nur Maimunah - Martono, Aris Mey Anggaraeni Muhamad Yusup Muhammad Dzulfikar Allam Muhammad Faisal Muhammad Fiqih Firmansyah Muhammad Rizky Awaluddin Mulyati Mulyati Mulyati Mulyati Nur Azizah Nurhaeni, Tuti Nursohit Nursohit Nursohit, Nursohit Nurviani Riska Suharto Oktaviani, Miatri Po Abas Sunarya Pratama, Amri Rahmat Hidayat Ramadhan, Gilang Kartika Hanum Ramadhan, Muhamad Khisbaeni Refauza Refauza Ria Wulandari Ridwan Alberto Pandiangan Rizki Maulana Kamil Rosalina Miliartha Rosdiana Rosdiana Ruli Supriati, Ruli Siti Dian Cahya Dwi Oktavionita Sofiana, Sofa Sudaryono Sudaryono Sudaryono Sudaryono Sugeng Santoso Suhaepi, Muhamad Iip Sulistiawati Sulistiawati Sulistiawati Sulistiawati Sumasih Sumasih Sunandar, Endang Suyatno Suyatno Swastika, Rulin Syafira Viglia Zumadilla Syarah Syarah Syarief Nur Husin Tia Wulandari, Anur Rahmah Tuti Nurhaeni

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