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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Informatika Universitas Pamulang PeTeKa IKRA-ITH ABDIMAS Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Tekinkom (Teknik Informasi dan Komputer) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JUKI : Jurnal Komputer dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Review of Practical Innovation, Technology and Green Energy (IRPITAGE) International Conference on Health Science, Green Economics, Educational Review and Technology (IHERT) Jurnal Penelitian Sistem Informasi Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi
Pakpahan, Victor Maruli
Universitas Mahkota Tricom Unggul
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Social Sciences Other

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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI PEMBANGUNAN HOTEL DENGAN METODE MULTI-ATTRIBUTIVE BORDER APPROXIMATION AREA COMPARISON Muhammad Yunus; Elsida Aritonang; Victor Maruli Pakpahan; Jubelando O Tambunan; Doris Yolanda Saragih
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1054

Abstract

This research aims to develop a Decision Support System (DSS) for determining the location of hotel development on Samosir Island. This DSS is constructed using the Multi-Attributive Border Approximation Area Comparison (MABAC) method. The criteria used in this study include Distance (C1), Natural Beauty (C2), Cleanliness (C3), Transportation (C4), and Atmosphere (C5). The choice of MABAC as the method is made because it provides flexibility for decision-makers to specify preferences and consider variations in criteria. The ranking results from MABAC indicate the top three locations, namely LH04, LH03, and LH01. By implementing the DSS using the MABAC method, this system is expected to make a significant contribution to optimizing the selection of hotel development locations in Samosir. The findings of this research illustrate that the use of the DSS with the MABAC method can be relied upon as a guide in addressing the impact of sustainable hotel growth in crucial tourist destinations such as Samosir.
Diagnosa Penyakit Epilepsi Menggunakan Metode Bayes Ade Rahayu; Achmad Fauzi; Victor Maruli Pakpahan
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.231

Abstract

Epilepsy, or apoplexy, is a chronic disease characterized by recurrent seizures and impaired consciousness due to disorders of the central nervous system. In developing countries, including in RSU Putri Bidadari, epilepsy management is often hampered by high consultation costs, resulting in suboptimal quality of treatment and patient recovery. To overcome this challenge, a system is needed that can facilitate the diagnosis and treatment of epilepsy more efficiently. By using this method, RSU Putri Bidadari can improve the precision of epilepsy diagnosis and determine more appropriate treatment steps, despite limited resources. The Bayes method, as a statistical approach, offers a potential solution to improve the accuracy of diagnosis through data-based probability estimation of diseases and symptoms reported by patients such as frequent hunger, thirst, urination, weight loss, vaginal infections, easy fatigue, tingling legs, and blurred vision. The analysis results of the system show an estimated probability of 73% for patients suffering from generalized epilepsy. The Bayes method-based system is expected to help RSU Putri Bidadari in providing more effective treatment and improving the overall quality of life of epilepsy patients.
Penerapan Metode Preference Selection Index (PSI) Penentuan Penilaian Kinerja Fasilitator di BBPPMPV BBL Medan Artika Suri Ayangda; Victor Maruli Pakpahan; Darjat Saripurna
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 4 (2024): Oktober: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i4.230

Abstract

Organizational performance includes various indicators such as productivity, work quality, efficiency, and innovation. Challenges often arise in the performance evaluation process when not supported by an adequate system. Additionally, facilitators play a crucial role in achieving goals, but poor selection of facilitators can lead to problems like lack of participation, poor time management, and inability to resolve conflicts, negatively impacting productivity and decision quality. This study aims to develop a support system that improves performance evaluation and facilitator roles at BBPPMPV-BBL Medan. The system is expected to simplify performance evaluation, enhance resource management effectiveness, and assist facilitators in carrying out their tasks optimally. The research findings indicate that this support system provides more accurate, structured, and effective performance evaluation, while enhancing facilitators' ability to maintain focus, manage time, and resolve conflicts. Implementing this system at BBPPMPV-BBL Medan contributes to increased productivity, efficiency, and performance quality, and enables proper recognition of high-performing individuals or teams.
CRYPTOCURRENCY AND DIGITAL MARKETING: LINKING FINANCIAL INNOVATION TO BUSINESS PERFORMANCE Jovanka Arnelita; Glen Bagasta Simanjuntak; Dompak Pasaribu; Victor Maruli Pakpahan
International Conference on Health Science, Green Economics, Educational Review and Technology Vol. 6 No. 1 (2024): 7th IHERT (2024): IHERT (2024) FIRST ISSUE: International Conference on Health
Publisher : Universitas Efarina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ihert.v6i1.431

Abstract

The development of financial technology, especially cryptocurrency and blockchain, has had a significant impact on various sectors, including digital marketing. The integration of cryptocurrency into digital marketing strategies provides new opportunities to improve marketing effectiveness and efficiency, build transparency, and strengthen relationships between companies and consumers. This study aims to explore how cryptocurrency and blockchain technology can be optimized in digital marketing to support business performance. The results show that blockchain technology enables more secure data management, transparent transaction management, and token-based loyalty programs that are attractive to consumers. In addition, the use of cryptocurrency in transactions can speed up the payment process, increase customer engagement, and strengthen brand loyalty. However, there are challenges in terms of regulation, consumer adoption of technology, and the need for specialized expertise in blockchain technology. This study provides an important overview of the potential and challenges of cryptocurrency integration in digital marketing and business performance, as well as providing practical advice for companies in implementing this technology.
Super Encryption of Rabin Cryptosystem Algorithm and Paillier Cryptosystem Algorithm on Digital Image Security Process Ramanda, Dika; Achmad Fauzi; Pakpahan, Victor Maruli
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.659

Abstract

Technological advances have given rise to the need for data protection, especially digital images, which are vulnerable to misuse. This research proposes a super encryption method that combines two cryptographic algorithms, namely Rabin Cryptosystem and Paillier Cryptosystem, to increase digital image security. Rabin's algorithm does not have homomorphism, so it is vulnerable to factorization attacks if the prime numbers used are too small. Meanwhile, the Paillier algorithm has homomorphism properties which allow arithmetic operations to be carried out directly on the ciphertext without decryption. By combining these two algorithms, this research aims to create a stronger and more efficient encryption method, and analyze its performance in terms of computational efficiency and complexity. It is hoped that the research results can improve the security and privacy of digital data, especially in the context of digital images.
MSMEs and the Role of Technology in Achieving Business Sustainability Sinuhaji, Frans Gidion; Sibarani, Hendra Jonathan; Pakpahan, Victor Maruli
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.724

Abstract

This study investigates the impact of Artificial Intelligence (AI) and Financial Technology (FinTech) on business sustainability among Micro, Small, and Medium Enterprises (MSMEs) in Medan City. Employing an associative research approach, the study utilizes quantitative data derived from both primary and secondary sources. The analysis methods include descriptive statistical analysis and Structural Equation Modeling (SEM). The study's population consists of 100 MSMEs in Medan City, selected through accidental sampling. The findings demonstrate that the adoption of AI and FinTech positively and significantly influences business sustainability. The integration of these technologies enhances operational efficiency, supports faster growth, and ultimately strengthens the sustainability of MSMEs. This research highlights the critical role of digital innovation in driving the development and resilience of small businesses, emphasizing the need for broader adoption of AI and FinTech solutions in the MSME sector to foster long-term growth and competitiveness.
Analisis Sentimen Berbasis Jaringan LSTM dan BERT terhadap Diskusi Twitter tentang Pemilu 2024 Muammar Khadapi; Pakpahan, Victor Maruli
JUKI : Jurnal Komputer dan Informatika Vol. 6 No. 2 (2024): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemilihan Umum (Pemilu) merupakan peristiwa politik penting yang memicu banyak diskusi di media sosial, terutama di platform seperti Twitter. Analisis sentimen dari diskusi ini dapat memberikan wawasan mengenai pandangan masyarakat terhadap calon, partai, serta isu-isu yang terkait. Penelitian ini berfokus pada penerapan dua model deep learning, yaitu Long Short-Term Memory (LSTM) dan Bidirectional Encoder Representations from Transformers (BERT), untuk menganalisis sentimen diskusi Twitter tentang Pemilu 2024. Kedua model ini dipilih karena kemampuan mereka dalam menangani data teks yang kompleks dan konteks bahasa alami. Dataset yang digunakan dalam penelitian ini terdiri dari ribuan tweet terkait Pemilu 2024, yang diklasifikasikan ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral. Data terlebih dahulu diproses melalui tahap pembersihan teks dan tokenisasi. Model LSTM dan BERT dilatih menggunakan dataset ini untuk memprediksi sentimen dengan fokus pada peningkatan akurasi prediksi. Hasil eksperimen menunjukkan bahwa model BERT secara konsisten memberikan performa yang lebih baik dibandingkan dengan LSTM. Model BERT berhasil mencapai akurasi validasi sebesar 76.48% pada epoch kedua, sedangkan model LSTM hanya mencapai akurasi maksimal 87 %. Meskipun demikian, model BERT mulai menunjukkan gejala overfitting pada epoch ketiga, dengan peningkatan nilai loss pada data validasi. Hal ini menunjukkan bahwa tuning lebih lanjut pada hyperparameter seperti jumlah epoch dan learning rate diperlukan untuk meningkatkan generalisasi model. Sementara itu, model LSTM menunjukkan stabilitas yang lebih baik, meskipun akurasinya lebih rendah, terutama dalam menangani dependensi konteks yang lebih sederhana. Secara keseluruhan, penelitian ini menegaskan bahwa model BERT lebih efektif dalam menangkap konteks kompleks pada teks Twitter terkait Pemilu 2024 dibandingkan dengan LSTM. Namun, tantangan seperti overfitting dan optimasi hyperparameter tetap menjadi perhatian utama. Untuk meningkatkan performa lebih lanjut, perlu dipertimbangkan teknik augmentasi data dan tuning hyperparameter yang lebih optimal. Penelitian ini juga membuka peluang untuk pengembangan model hibrida yang menggabungkan keunggulan LSTM dan BERT dalam analisis sentimen berbasis teks.
Diagnosa Penyakit Kanker Prostat menggunakan Metode Certainty Factor Nezha Febriyan; Achmad Fauzi; Victor Maruli Pakpahan
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 5 (2024): Oktober : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i5.341

Abstract

The rapid development of information technology affects the way people access information, including in the health sector. Prostate cancer, as one of the most significant types of cancer in men, is often detected late due to lack of information and limited costs. To overcome this problem, a system is needed that is able to diagnose prostate cancer quickly, precisely, and accurately. This study aims to develop a web-based expert system using the Certainty Factor (CF) method to diagnose prostate cancer based on the symptoms that appear. The CF method was chosen because of its ability to determine the level of confidence in the facts or rules used in the diagnosis. This study uses data on symptoms and types of prostate cancer. The results of the study can help the public in recognizing prostate cancer symptoms early, with a high level of accuracy in diagnosis. This study is expected to make it easier for patients to make an early diagnosis and accelerate the treatment of prostate cancer.
“Klasifikasi Citra Penyakit Gigi Menggunakan Metode K-Nearest Neighbor”. Sri Dewi Novita; Achmad Fauzi; Victor Maruli Pakpahan
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.244

Abstract

Handling of dental disease problems requires that it be handled quickly and correctly, but not all teams of dental experts can carry out treatment quickly due to the lack of a team of dental experts who are in the workplace or hospital 24 hours a day. Apart from that, the public also has very little knowledge of information about dental disease, so that to treat dental disease, people have to consult a dentist. To classify images of dental disease, feature extraction is needed. Feature extraction is taking characteristics of an object that can describe the image. One example of image feature extraction used is Red, Green, Blue (RGB). This feature extraction is often used to identify or classify an image. Dental image data that will be used in the classification process are tooth abrasion, anterior crosbite, cavities and gingivitis. K-Nears Neigbor is the simplest data mining algorithm. The aim of this algorithm is to find the results of the closest distance classification for each object. In determining the distance, the data is initially divided into two parts, namely training data and testing data. After receiving the training data and testing data, the distance from each testing data (Equilidence Distance) to the training data is calculated. The K-Nearest Neighbors method can be applied to classify dental disease based on images of types of dental disease using Matlab software. As a result of the image data training process, 40 image data were input, training results obtained were 100%.
Penerapan Metode Clustering pada Status Gizi Ibu Hamil : (Studi Kasus: Puskesmas Kota Datar) Hesty Vitara; Rusmin Saragih; Victor Maruli Pakpahan
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 4 (2024): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i4.321

Abstract

Pregnancy is a process in a woman's life, where major changes occur in her physical, mental and social aspects. These changes cannot be separated from the factors that influence them, namely physical factors, psychological factors and environmental, social, cultural and economic factors. One of the nutritional problems of pregnant women is chronic energy deficiency (KEK). Chronic energy deficiency (KEK) is a nutritional problem caused by a lack of food intake over a long period of time, a matter of years. Datar City Health Center is one of the agencies that provides health services for the local community and helps resolve problems with the health and nutritional development of mothers and children to prevent problems with malnutrition in pregnant women. The aim of the research is to make it easier for agencies to manage data and obtain complete information about the nutritional status of pregnant women. From 20 data, 3 groups were obtained, Cluster 1 had 4 data on the nutritional status of pregnant women, Cluster 2 had 4 data on the nutritional status of pregnant women and Cluster 3 had 12 data on the nutritional status of pregnant women. And the largest group obtained was cluster 3 with the data group on the nutritional status of pregnant women found in the gestational age group (X), namely 14-27 weeks old, with screening results (Y) namely adequate nutrition, and the causal factors (Z) that occurred were economic factors
Co-Authors ACHMAD FAUZI Ade Rahayu Artika Suri Ayangda Danianty Miranda Br. Bangun Darjat Saripurna Debora Tambunan Dewi Rafiah Pakpahan Doris Yolanda Saragih Elsida Aritonang Farida Gultom Fauzi, Achmad Frans Gideon Sinuhaji Glen Bagasta Simanjuntak Grestin Ekalina Turnip Hendra Jonathan Sibarani Hesty Vitara Jen Peng Huang Jovanka Arnelita Jubelando O Tambunan Marto Sihombing Mhd Arif Permata Muammar Khadapi Muhammad Yunus Nezha Febriyan Nova Erawati Sidabalok Novi Yunanda Putri Ramanda, Dika Rusmin Saragih, Rusmin Salim Efendi Sanjaya Pinem Saut Matedius Situmorang Saut Matedius Situmorang Sibarani, Hendra Jonathan Sinuhaji, Frans Gidion Sitorus, Dompak Sri Dewi Novita Tony Blayer Simangunsong Viktor Edison Hutagaol Widya Natasya Yani Maulita