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Sitorus, Zulham
UNIVERSITAS PEMBANGUNAN PANCA BUDI
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Journal : Bulletin of Engineering Science, Technology and Industry

 1 
OPTIMIZATION OF WIFI NETWORK IN KUALANAMU INTERNATIONAL AIRPORT Hendra Harnanda; Andysah Putra Utama Siahaan; Leni Marlina; Muhammad Iqbal; Zulham Sitorus
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 4 (2024): December
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i4.67

Abstract

A reliable and efficient WiFi network is a crucial facility to support the activities of passengers and visitors at Kualanamu International Airport. This study aims to identify issues affecting WiFi network performance and provide optimization solutions based on technical analysis and user needs. Data collection was conducted by measuring network parameters such as throughput, delay, jitter, and packet loss across various strategic airport areas, including waiting rooms, departure terminals, and commercial zones. The analysis revealed key challenges, such as high user density during peak hours and uneven access point distribution. To address these issues, the study proposes optimization steps, including bandwidth capacity expansion, reconfiguration of network devices, and the adoption of advanced technologies like WiFi 6. Implementing these recommendations is expected to enhance the quality of the WiFi network at Kualanamu International Airport, improve user experience, and support overall airport operations effectively.
ANALYSIS OF THE LEVEL OF EFFECTIVENESS OF THE INDEPENDENT CAMPUS MERDEKA LEARNING PROGRAM (MBKM) USING METHODSPREFERENCE SELECTION INDEX (PSI) AND VIKOR METHOD Kiki Artika; Muhammad Iqbal; Zulham Sitorus; Andysah Putera Utama Siahaan; Rian Farta Wijaya
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.61

Abstract

This research aims to analyze the level of effectiveness of the Independent Campus Learning Program (MBKM) using the Preference Selection Index (PSI) Method and VIKOR Method. The MBKM program is an initiative of the Ministry of Education and Culture of the Republic of Indonesia which aims to provide more flexibility and learning opportunities for students through various off-campus activities. This research was conducted to measure the extent to which the program succeeded in achieving its goals. The PSI method is used to determine preferences for various aspects of the program based on assessments from students and academic staff, while the VIKOR method is used to identify the best compromise solution that can maximize stakeholder satisfaction. Analysis was carried out to assess the effectiveness of the program based on several criteria, including the quality of the learning experience, relevance to the world of work, and contribution to student skills development. This research suggests that to further increase the effectiveness of the MBKM Program, there needs to be an emphasis on developing a curriculum that is more responsive to industry needs and improving supporting facilities for students. The implications of the results of this research are important for policy makers in designing educational strategies that are more adaptive and oriented to labor market needs.
MACHINE LEARNING ANALYSIS IN IMPROVING THE EFFICIENCY OF THE STUDENT ADMISSION DECISION MAKING PROCESS NEW AT PANCA BUDI MEDAN DEVELOPMENT UNIVERSITY M. Rasyid; Zulham Sitorus; Rian Farta Wijaya; Muhammad Iqbal; Khairul
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.62

Abstract

The decision-making process in admitting new students is a crucial aspect that can influence the quality and efficiency of academic administration in higher education. This research aims to analyze the role of Machine Learning methods, especially Support Vector Machines (SVM), in increasing the efficiency of the decision-making process for new student admissions at the Panca Budi Development University, Medan. The data used in this research includes information from the student admissions process for the odd semester of the 2022/2023 academic year, which includes various variables such as Registration Number, School of Origin, Registration Payment, and others. The data is divided into a training set (70%) and a testing set (30%). The Support Vector Machine (SVM) model that was built was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The research results show that the SVM model achieves an accuracy of 100%, with high precision and recall for both classes. Precision for both classes reached 1.00, while recall for the minority class (class 1) reached 0.91, indicating excellent model performance in classification. The conclusion of this research is that the Support Vector Machine (SVM) model can significantly increase efficiency and accuracy in the decision-making process for new student admissions at the Panca Budi Development University in Medan compared to conventional methods. These findings indicate that the application of Machine Learning methods can provide substantial benefits in the context of academic administration.
ANALYSIS OF GOOGLE USER SENTIMENT TOWARDS UNIVERSITAS PEMBANGUNAN PANCA BUDI BASED ON REVIEWS GOOGLEUSING THE NAÏVE BAYES ALGORITHM M Imam Santoso; Rian Farta Wijaya; Zulham Sitorus; Muhammad Iqbal; Leni Marlina
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.63

Abstract

This thesis examines user sentiment towards Panca Budi Development University by utilizing Google reviews as the main data and using the Naïve Bayes algorithm for sentiment analysis. This research aims to understand the public's perception of the university through reviewing reviews available on the Google platform. The data used consists of user reviews collected from Google Reviews. The analysis process begins with data pre-processing, including text cleaning and tokenization, followed by the development of a Naïve Bayes model for classification of review sentiment into positive, negative, or neutral categories. The results of this analysis provide insight into the strengths and weaknesses of Panca Budi Development University from a user perspective, as well as identifying areas that require improvement. It is hoped that these findings can become a basis for the university to improve the quality of its services and reputation in the eyes of the public. This research also highlights the effectiveness of the Naïve Bayes algorithm in sentiment analysis, and contributes to further studies on sentiment analysis in the education sector
Co-Authors , Arpan A.A. Ketut Agung Cahyawan W Abdul Karim Afrizal, Sandi Akbar Maulana, Taufik Aldi Kesuma Alvian Alvian Andi Ernawati Andysah Putera Utama Siahaan Angkat, Chairul Indra Antoni, Robin Ardya, Dwika Arief, Muhammad Arif Rahman Asyahri Hadi Nasyuha Aulia, Ananda Ayu Ofta Batubara, Supina Br Tarigan, Sella Monika Daniel Happy Putra Danu Wardhana Azhari Darmeli Nasution DEWI SARTIKA diansyah, Suhar Eko Hariyanto Eko Hariyanto Eko Hariyanto Fahmi Iskandar Fahmi Izhari Fahmi Kurniawan Farta wijaya, Rian Faza Wardanu Damanik, Dwi Fikri Zuhaili Simbolon Gilang Ramadhan Gultom, Ananda Christianto H. Aly, Moustafa Hafiz Rodhiy Haliza, Siti Nur Helmy, Ahmad Hendra Harnanda Heni Wulandari Hrp, Abdul Chaidir Ibezato Zalukhu, Anzas Ika Devi Perwitasari Indra Angkat, Chairul IQBAL , MUHAMMAD Irwan Syahputra Irwan Syahputra, Irwan Iswadi Hamzah Khairul Khairul Khairul Khairul, Khairul Kiki Artika Kurniawan, Fahmi Larius Ambasador Parlindungan Leni Marlina Leni Marlina Limbong, Yohannes France M Imam Santoso M. Rasyid M.Rizki Khadafi Mardiah, Nia Melva Sari Panjaitan Meri Sri Wahyuni Mhd Arie Akbar Mohammad Yusuf, Mohammad Muhammad Iqbal Muhammad Iqbal Muhammad Wahyudi Nahampun, Natalia Nainggolan, Andreas Ghanneson Nasution, Darmeli Nazar Saputra, Risfan Ofta Sari, Ayu Parhusip, Nelviony Pranoto, Sugeng Putra, Khairil Ragil Satya Adi W Ramadani, Pebri Ramadhan, Aditya Ramadhan, Deni Ramadhani, Aditya Rian Farta Wijaya Rian Putra, Randi Risky, Raihan Rusydi Tanjung , Miftah Sahputra, Fajar Said Oktaviandi Saputra, Maulian Sari Penjaitan, Melva Septiani, Nadya Sianturi, Ismail Sibarani, Dina Marsauli Simamora, Siska Simorangkir, Elsya Sabrina Asmita Sinambela, Sugi Hartono Sinyo Andika Nasution, Ahmad Siregar, Andree Risky Yuliansyah Sitepu, Fernando Sitinur, Siti Nurhaliza Sofyan Sitompul, Jelly Rolley Sofyan, Siti Nurhaliza Solly Ariza Lubis Suhardiansyah Suhardiansyah Suhardiansyah Suherman Suherman Sukrianto, Sukrianto Sutiono, Sulis Syahputri, Maulisa T, Siti Isna Syahri Tanjung, Miftah Rusydi Utama, Hendra Vina Arnita Vivin Yulfia Sarah Wahyu Agung Pratama Wahyuni, Meri Sri Wijaya, Rian Farta Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Wirda Fitriani Yahya, Susilawati Zalukhu, Anzas Ibezato Zulfahmi Syahputera Zulfahmi Zulfahmi Zulfahmi Zulfahmi