Article Per Year (5 Year)
p-Index From 2020 - 2025
0.947
P-Index
This Author published in this journals
All Journal Journal Sensi: Strategic of Education in Information System MAVIB Journal : Jurnal Multimedia Audio Visual and Broadcasting Blockchain Frontier Technology (BFRONT)
Suhaepi, Muhamad Iip
Unknown Affiliation
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Library & Information Science Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
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

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.
Blockchain Technology for Cashless Investments and Transactions in Digital Era With SWOT Approach Yusup, Muhamad; Sukmawati, Eva; Ramadhan, Rezki; Suhaepi, Muhamad Iip; Zebua, Selamat; Amallia, Naila
Blockchain Frontier Technology Vol. 2 No. 1 (2022): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v2i1.91

Abstract

One of the most visible information technologies in today's digital age is transaction and investment technology, where users or the public are facilitated in transactions by no longer using cash. Users also feel safer because they do not need to carry cash. Moreover, the current digital era makes it easy for users or the public to invest anywhere and anytime without disrupting other businesses. Blockchain technology can be used as an alternative to non-payment transactions or commonly known as cashless transactions. Blockchain technology can also be used as an investment in electronic money. The methodology used to provide an overview and evaluate the benefits of blockchain as an alternative to digital payment and investment activities in the cryptocurrency industry uses a SWOT approach. Based on the SWOT approach that has been taken, it can be concluded that with internationally recognized safeguards of confidentiality and the ease of conducting investment transactions and activities without payment, the Weaknesses and threats can be controlled for more investors to enter the world of electronic money.
Pemanfaatan Sosial Media Instagram Sebagai Sarana Promosi Wilayah KIM Urang Baraya Immaniar Desrianti, Dewi; Suhaepi, Muhamad Iip; Ramadhoni, Fiqri; Ridwan, Muhamad
Jurnal MAVIB Vol 6 No 1 (2025): MAVIB Journal - Februari 2025
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mavib.v6i1.3222

Abstract

KIM Urang Baraya menghadapi tantangan dalam melakukan promosi kepada masyarakat di tengah perkembangan teknologi informasi. Meskipun mereka menggunakan Instagram sebagai saluran komunikasi, namun belum begitu efektif dalam melakukan promosi tentang profil dan kegiatan KIM Urang Baraya. Kajian ini menyoroti permasalahan tersebut dan berupaya memberikan solusi dengan idea desain, ilustrasi, gambar, dan foto, dikombinasikan dengan layout dan foto, dapat meningkatkan minat masyarakat. Metode dari penelitian ini terdiri: analisa permasalahan, pengumpulan data, analisa perancangan media dan ide desain. Sosial media Instagram KIM Urang Baraya mempromosikan tentang: profil, manfaat, program kerja, fasilitas, kegiatan, prestasi, dan relasi, dirancang menggunakan Adobe Photoshop CS 6. Kami berharap social media Instagram dapat diakses oleh semua Masyarakat Kota Tangerang. Melalui social media Instagram ini dapat membantu KIM Urang Baraya, dalam melakukan promosi secara lebih lengkap, menarik, up to date, dikenal dan berguna bagi penduduk Kota Tangerang.
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

Abstract

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

Abstract

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.
Co-Authors Amallia, Naila Desrianti, Dewi Immaniar Martono, Aris Muhamad Ridwan Muhamad Yusup Padeli Padeli Ramadhan, Rezki Ramadhoni, Fiqri Sugeng Santoso Sukmawati, Eva Sunandar, Endang Tia Wulandari, Anur Rahmah Zebua, Selamat