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All Journal Dinamik Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah FIFO ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Paradigma Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Ilmiah Informatika dan Ilmu Komputer CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Bulletin of Artificial Intelligence Jurnal Informatika dan Rekayasa Perangkat Lunak
Aditia Yudhistira
Universitas Teknokrat Indonesia
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DYNAMIC WEIGHT ALLOCATION IN MODIFIED MULTI-ATRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS WITH SYMMETRY POINT FOR REAL-TIME DECISION SUPPORT Hadad, Sitna Hajar; Chandra, Iryanto; Wang, Junhai; Megawaty, Dyah Ayu; Setiawansyah, Setiawansyah; Yudhistira, Aditia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4170

Abstract

Decision Support Systems (DSS) have a crucial role in real-time decision-making, especially in the digital era that demands high speed and accuracy. Managing criterion weights in a dynamic environment presents significant challenges due to rapid and unpredictable changes in conditions. However, determining an accurate weight becomes difficult due to uncertainty, incomplete data, and subjective factors from decision-makers. In addition, changes in the external environment, such as market trends, regulations, or customer preferences, can affect the relevance of each criterion, thus requiring a real-time weight adjustment mechanism. The purpose of this study is to develop and explore the dynamic weight allocation method in symmetry point- multi-attributive ideal-real comparative analysis (S-MAIRCA) to support more accurate and responsive real-time decision-making in a dynamic environment. This research contributes to the understanding of how the weights of criteria can be adjusted automatically and responsively to changing conditions or new data, which increases the relevance and accuracy of decisions in a dynamic environment. The urgency of S-MAIRCA research is important because it often involves real-time, dynamic, and complex data. This development not only improves the adaptability of the S-MAIRCA method, but also contributes significantly to creating computer science-based applications that are more intelligent, flexible, and relevant to the evolving needs of the system. The results of the alternative ranking comparison using the CRITIC-MAIRCA, LOPCOW-MAIRCA, ROC-MAIRCA, and S-MAIRCA methods showed variations in the ranking order generated for each alternative using spearman correlation. The results of the correlation value of CRITIC-MAIRCA and LOPCOW-MAIRCA have a very high correlation of 0.993, which shows that these two methods provide almost identical rankings in alternative evaluation. Likewise, CRITIC-MAIRCA and S-MAIRCA had a high correlation of 0.979, signaling a strong similarity in ranking results despite slight differences in the approaches used by the two methods. The results of the application of the MAIRCA-S method in the development of DSS based on real-time data have a significant impact on improving the speed, accuracy, and adaptability of decisions. MAIRCA-S strengthens the validity of decision results by considering a variety of attributes on a more comprehensive scale, providing added value in the development of DSS for various industrial sectors.
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Rahmanto, Yuri; Wang, Junhai; Setiawansyah, Setiawansyah; Yudhistira, Aditia; Darwis, Dedi; Suryono, Ryan Randy
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Evaluasi Opini Publik di Media Sosial X terhadap Kebijakan Pajak Pertambahan Nilai 12% di Indonesia Menggunakan Naive Bayes dan Decision Tree Adamansyah, Eka Putri; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.710

Abstract

Penerapan kebijakan Pajak Pertambahan Nilai (PPN) 12% di Indonesia telah memicu beragam tanggapan dari masyarakat, khususnya di media sosial. Penelitian ini bertujuan untuk mengevaluasi pandangan publik terhadap kebijakan tersebut dengan memanfaatkan data dari media sosial X. Data dikumpulkan melalui teknik crawling, menghasilkan 1.815 tweet yang relevan dengan diskusi mengenai PPN 12%. Tahapan analisis meliputi preprocessing data serta pembobotan kata menggunakan Term Frequency-Inverse Document Frequency (TF-IDF), diikuti dengan klasifikasi sentimen menggunakan algoritma Naive Bayes dan Decision Tree. Hasil penelitian menunjukkan bahwa meskipun algoritma Decision Tree memiliki akurasi yang lebih tinggi (93,44%) dibandingkan Naive Bayes (92,68%), namun Naive Bayes lebih efisien dalam menangani dataset yang lebih besar. Dari seluruh tweet yang dianalisis, 94,54% mengandung sentimen negatif terkait kekhawatiran tentang dampak ekonomi dan peningkatan beban pajak, sementara 5,46% mengandung sentimen positif yang umumnya menyoroti potensi peningkatan penerimaan negara dan pembangunan. Penelitian ini menyediakan wawasan bagi pemerintah dalam memahami persepsi publik serta merancang strategi komunikasi yang lebih efektif terkait kebijakan perpajakan.
Analisis Sentimen Petani Milenial Pada Media Sosial X Menggunakan Algortitma Support Vector Machine (SVM) Ma'rufudin; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.717

Abstract

Media sosial, termasuk aplikasi X, kini menjadi platform utama untuk berbagai diskusi, termasuk topik terkait pertanian milenial. Meski demikian, masih terdapat perbedaan pandangan mengenai penerapan teknologi di sektor pertanian oleh petani milenial. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap petani milenial dengan menggunakan algoritma Support Vector Machine (SVM). Sebanyak 2.430 tweet dikumpulkan melalui teknik crawling dan diproses melalui tahapan preprocessing data, seperti tokenisasi, normalisasi, penghapusan stopword, dan stemming, serta diberikan bobot menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Model SVM yang dikembangkan dalam penelitian ini mengklasifikasikan sentimen ke dalam tiga kategori: positif, netral, dan negatif. Hasil eksperimen menunjukkan bahwa model SVM mencapai akurasi 70% dengan rata-rata F1-score sebesar 0,69. Model ini memiliki precision tertinggi sebesar 0,72 untuk sentimen negatif dan recall tertinggi sebesar 0,84 untuk sentimen positif. Dibandingkan dengan algoritma Naïve Bayes yang hanya memperoleh akurasi 65%, SVM terbukti lebih efektif dalam analisis sentimen berbasis teks. Temuan ini mengindikasikan bahwa SVM dapat digunakan untuk mengidentifikasi sentimen publik terhadap petani milenial dengan lebih akurat.
Pengembangan Sistem Pelaporan Keuangan Berbasis Web Menggunakan Metode Waterfall Untuk Meningkatkan Transparansi Pengelolaan Dana di MTS MA Margodadi niswatun umami, Nila; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 4 (2025): JPTI - April 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.725

Abstract

Pendidikan memiliki peran yang sangat penting dalam meningkatkan kualitas individu, sesuai dengan Undang-Undang Nomor 20 Tahun 2003 yang menekankan pengembangan potensi diri dan pembentukan karakter. Di Indonesia, tantangan dalam pengelolaan pembiayaan pendidikan semakin meningkat, terutama di tengah keterbatasan anggaran dan kebutuhan yang terus bertambah. MTS MA Margodadi berupaya mengatasi tantangan ini melalui pengelolaan dana yang efektif. Penelitian ini bertujuan untuk mengembangkan sistem manajemen pendidikan berbasis web yang dapat meningkatkan transparansi dan akuntabilitas dalam pengelolaan keuangan sekolah. Sistem ini dikembangkan menggunakan metode Waterfall dengan pemrograman PHP dan database MySQL. Dengan sistem ini, diharapkan pengelolaan keuangan menjadi lebih efisien, mengurangi risiko kesalahan dari sistem manual, serta mempercepat penyusunan laporan keuangan. Kerjasama antara semua elemen di sekolah, termasuk kepala sekolah, bendahara, dan guru, sangat penting untuk memastikan pengelolaan keuangan yang baik. Penelitian ini menggunakan metode observasi dan wawancara untuk mengumpulkan data, serta analisis kebutuhan untuk merancang sistem yang sesuai. Hasil penelitian menunjukkan bahwa sistem informasi ini dapat meningkatkan akurasi pencatatan keuangan hingga 95%, meningkatkan akurasi dan transparansi laporan keuangan, membangun kepercayaan antara pihak terkait, dan mencegah penyimpangan dalam penggunaan dana, serta mempercepat proses audit keuangan sekolah. Dengan demikian, penelitian ini memberikan kontribusi signifikan terhadap peningkatan kualitas pendidikan di MTS MA Margodadi dan lembaga pendidikan lainnya di Indonesia, serta menawarkan solusi untuk tantangan pengelolaan keuangan yang dihadapi oleh sekolah-sekolah.
Implementasi SPK Metode SAW untuk Menentukan Guru Terbaik di SMPN 14 Tulang Bawang Barat Luthfi Falih, Yudep Rafidal; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 8 (2025): JPTI - Agustus 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.959

Abstract

Penilaian kinerja guru merupakan elemen kunci dalam peningkatan mutu pendidikan, namun proses evaluasi sering mengalami kendala karena kurangnya sistem yang objektif dan terstruktur. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis metode Simple Additive Weighting (SAW) guna menentukan guru terbaik secara objektif di SMP Negeri 14 Tulang Bawang Barat. Metode SAW digunakan karena kemampuannya dalam mengolah data multikriteria melalui proses normalisasi dan pembobotan. Kriteria penilaian meliputi absensi, jam mengajar, disiplin, tugas tambahan, dan pengembangan profesi. Data diperoleh dari observasi dan wawancara dengan pihak sekolah. Sistem yang dibangun menghasilkan peringkat guru berdasarkan skor Vi, di mana guru dengan kode A2 memperoleh nilai tertinggi sebesar 0,800. Hasil ini menunjukkan keefektifan SAW dalam mendukung evaluasi kinerja yang adil dan transparan. Sistem ini berkontribusi dalam meningkatkan akuntabilitas penilaian serta mendukung pengambilan keputusan yang lebih tepat dalam pengelolaan sumber daya manusia di sekolah.
Penerapan Metode Simple Additive Weighting dalam Pengembangan Sistem Pendukung Keputusan Seleksi Pemain Futsal pada Liga Nusantara Futsal Bondpeace di Provinsi Lampung Yansyah, Dery; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1027

Abstract

Liga Futsal Nusantara (LFN) merupakan kompetisi futsal tingkat nasional yang bertujuan menjaring talenta muda dan meningkatkan kualitas futsal di Indonesia. Club Futsal Bondpeace dari Bandar Lampung menghadapi tantangan dalam proses seleksi pemain yang objektif dan terstruktur. Penelitian ini bertujuan mengembangkan sistem pendukung keputusan seleksi pemain menggunakan metode Simple Additive Weighting (SAW). Metode SAW digunakan untuk menghitung nilai total dari setiap alternatif berdasarkan kriteria dan bobot yang telah ditentukan, sehingga memungkinkan proses perankingan yang transparan. Hasil penelitian menunjukkan bahwa penerapan metode SAW menghasilkan keputusan seleksi yang lebih akurat dan objektif dibandingkan metode konvensional. Temuan ini menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan kualitas seleksi pemain serta mendukung pengambilan keputusan manajemen tim secara lebih tepat dan efisien [1].
Perbandingan Algoritma Naive Bayes, Random Forest, dan Support Vector Machine Terhadap Pandangan Masyarakat Mengenai Revisi Undang-Undang TNI di Instagram Nasrul, Royhan; Yudhistira, Aditia
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8164

Abstract

The revision of the Indonesian National Army Law (TNI Law), enacted in 2025, sparked widespread controversy within society, particularly concerning issues of civilian supremacy and potential military dominance. With the growing use of social media as a platform for public expression, platforms such as Instagram have become the primary medium for the public to voice their opinions regarding this issue. This study aims to analyze public sentiment toward the revision of the TNI Law by utilizing text classification algorithms, namely Naive Bayes, Random Forest, and Support Vector Machine (SVM). Data was collected from 28,669 Instagram comments and analyzed through stages of data crawling, preprocessing, and labeling. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Subsequently, classification was performed using the three algorithms, with evaluation metrics including accuracy, precision, recall, and F1-score. The results after SMOTE demonstrated that the SVM algorithm delivered the best performance with an accuracy of (92%), followed by Random Forest at (88%), and Naive Bayes at (76%). Consequently, SVM was deemed the most effective in capturing patterns of public sentiment objectively. This research is expected to contribute to the advancement of digital public opinion studies and support the evaluation process of national defense policies
Hybrid Logarithmic Percentage Change-Driven Objective Weighting and Grey Relational Analysis Method in Employee Contract Renewal Setiawansyah, Setiawansyah; Rahmanto, Yuri; Aldino, Ahmad Ari; Yudhistira, Aditia; Palupiningsih, Pritasari; Sulistiyawati, Ari
TIN: Terapan Informatika Nusantara Vol 4 No 12 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i12.5121

Abstract

Contract employees are individuals who are hired for a specific period of time within a company or organization for a specific purpose. They usually do not have permanent employee status and are bound by work contracts that govern their tenure, salary, and other obligations. Despite not having long-term job security, contract employees often bring specialized skills or experience needed for specific projects. They are often instrumental in handling temporary projects, fulfilling temporary company needs, or filling temporary vacancies. One of the main problems in determining employee contract renewal is the lack of transparency and clear communication from the company. Employees often feel confused or uncertain about the criteria used by management in determining whether or not their contract will be renewed. Lack of clear information can cause anxiety and uncertainty among employees, and impair their performance and motivation. Hybrid Logarithmic Percentage Change-Driven Objective Weighting and Grey Relational Analysis (HLOPCOW-GRA) is an approach that combines two analysis methods, namely LOPCOW and GRA to improve accuracy and reliability in decision making. HLOPCOW-GRA provides an advantage in combining LOPCOW's advantage in handling dynamic data fluctuations with GRA's advantage in analyzing relative relationships between criteria, this approach allows decision makers to gain a deeper understanding of the factors that affect the final outcome. The results of alternative ranking showed that the first place with a GRA final value of 0.1406 was obtained by EM alternatives, second place with a GRA final value of 0.1366 was obtained by SVR alternatives, third place with a GRA final value of 0.1366 was obtained by SVR alternatives, third place with a GRA final value of 0.1406 was obtained by EM alternatives. The final GRA value of 0.1245 obtained alternative ASR.
COMBINATION OF LOGARITHMIC PERCENTAGE CHANGE-DRIVEN OBJECTIVE WEIGHTING AND MULTI-ATTRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS IN DETERMINING THE BEST PRODUCTION EMPLOYEES Hadad, Sitna Hajar; Subhan, Subhan; Setiawansyah, Setiawansyah; Arshad, Muhammad Waqas; Yudhistira, Aditia; Rahmanto, Yuri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2057

Abstract

The problem that occurs in the selection of the best production employees is the lack of transparency and objectivity in the selection process. Without clear procedures and well-defined criteria, employee selection decisions can be influenced by subjective preferences or irrelevant non-performance factors. This can result in injustice in employee selection and lower the morale and motivation of unselected employees. The purpose of the combination of LOPCOW and MAIRCA in determining the best production employees is to provide a holistic and adaptive framework in the employee performance evaluation process. LOPCOW allows decision makers to dynamically adjust the weight of criteria according to the level of volatility or change in the relevant environment or situation. LOPCOW offers an adaptive and responsive approach in determining the weight of criteria, enabling decision makers to respond quickly to changes occurring in the relevant environment or situation. MAIRCA is an analytical method used to assist decision makers in evaluating and selecting alternatives based on several relevant criteria or attributes. MAIRCA provides a strong framework for decision makers to make more informed and informed decisions. Combining these two methods results in a more comprehensive and accurate understanding of production employee performance, thus enabling managers to identify the most effective employees and provide rewards or development accordingly. The final results of the ranking of the best production employees obtained by JR employees get 1st place, YP employees get 2nd place, and AJL employees get 3rd place.
Co-Authors Adamansyah, Eka Putri Ahmad Ari Aldino Akil Thalib Anggoro, Bayu Ari Sulistiyawati Arshad, Muhammad Waqas Asistyasari, Ayuni Chandra, Iryanto Dedi Darwis Gustina, Intan Hafizhah, Hafizhah Handika, Tegar Dio Hari Agung Adrianto Imas Sukaesih Sitanggang KHADAFI, MUHAMMAD Luthfi Falih, Yudep Rafidal Ma'rufudin Mahmudah, Siti Anisa Maria Angelina Luisa Makin Megawaty, Dyah Ayu Nasrul, Royhan niswatun umami, Nila Oprasto, Raditya Rimbawan Palupiningsih, Pritasari Pasaribu, A. Ferico Octaviansyah Putri, Clara Indriani Ryan Randy Suryono Saidina Ali Habib Sanjaya, M. Alung Setiawansyah Setiawansyah Sitna Hajar Hadad Subhan Subhan Suci Fitrianti, Suci Sumanto Sumanto Tri Widodo Ulum, Faruk Wahyudi, Agung Deni Wang, Junhai Yansyah, Dery yasin, ikbal Yosep Nuryaman Yuri Rahmanto