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All Journal Jurnal Ilmiah FIFO KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia 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
Aditia Yudhistira
Universitas Teknokrat Indonesia
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Analisis Klasterisasi Penilaian Kinerja Pegawai Menggunakan Metode Fuzzy C-Means (Studi Kasus : Pengadilan Tinggi Agama bandar lampung) Aditia Yudhistira; Ahmad Ari Aldino; Dedi Darwis
EDUTIC Vol 9, No 1 (2022): November 2022
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.862 KB) | DOI: 10.21107/edutic.v9i1.17134

Abstract

Data mining merupakan teknik pengolahan Data dalam jumlah  besar untuk pengelompokan. Teknik data mining mempunyai beberapa metode dalam mengelompokan salah satunya tekn ik yang dipakai penulis saat ini adalah Fuzzy C-means. dalam hal ini penulis akan mengelompokan Penilaian kinerja pegawai bertujuan untuk mengevaluasi kinerja pegawai  dan pemberian apresiasi terhadap pegawai Yang memiliki kinerja baik, guna meningkatkan semangat pegawai dalam bekerja. Penilian kinerja pegawai dilakukan dengan menjumlahkan nilai tiap kriteria penilaian dan menggunakan standar nilai untuk menentukan nilai akhir. Pada penelitian ini penelitian kinerja pegawai yang digunakan adalah nilai perilaku yaitu nilai orientasi, nilai integritas, nilai komitmen, nilai kedisiplinan dan nilai kerjasama.  Nilai tersebut diolah dengan menggunakan metode Fuzzy C-Means (FCM) dengan tools aplikasi matlab sehingga menghasilkan sejumlah kelompok karyawan yang memiliki standar penilaian bersifat dinamis.  Penetapan nilai yang di peroleh pegawai didasarkan pada pengurutan pusat cluster hasil pengolahan total nilai pegawai menggunakan FCM. Pada Penelitian ini berhasil dikelompokan pegawai dengan kelompok pegawai yang termasuk sangat baik, baik, cukup, kurang dan buruk. Dari hasil analisis pengelompokan FCM dengan 5 cluster dengan 35x iterasi  diperoleh fungsi objektif sebesar 111.949781. Dimana  kelompok  pertama terdiri dari 940 pegawai, klaster ke dua 692 pegawai, kelompok 23 pegawai , kelompok ke empat   terdiri dari 8 pegawai dan kelompok ke lima terdiri dari 17 pegawai. Dari hasil centroid ini telah dianalisa bahwa rata-rata nilai pegawai memiliki nilai sangat baik pada fitur nilai komitmen dan nilai kesidipilinan. Hal ini dapat menjadi pola bahwa seorang pegawai dikatakan layak untuk mendapatkan reward ketika nilai komitmen dan nilai kedisiplinan sangat baik.
Sistem Pendukung Keputusan Rekomendasi Hotel Bintang Tiga Menggunakan Kombinasi Entropy dan Combine Compromise Solution Wahyudi, Agung Deni; Sumanto, Sumanto; Setiawansyah, Setiawansyah; Yudhistira, Aditia
Bulletin of Artificial Intelligence Vol 3 No 1 (2024): April 2024
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v3i1.142

Abstract

Three Star Hotels are lodging places that offer the perfect balance between comfort, adequate facilities, and affordable prices. With a friendly atmosphere and professional service, the hotel welcomes guests from various backgrounds warmly. One of the problems in choosing a Three Star Hotel is confusion due to variations in quality and facilities among hotels that have similar ratings. Although they share the same categories, the standards and services offered can vary greatly. This can make potential guests find it difficult to choose the right hotel that suits their preferences and needs. In addition, some hotels may not meet guest expectations due to issues such as poor cleanliness or facilities that do not function properly, which can generate dissatisfaction. The combination of Entropy weighting and the Combine Compromise Solution method can be a powerful approach in providing three-star hotel recommendations to potential guests. By combining these two methods, it can produce more informed and objective three-star hotel recommendations. Entropy weighting helps in assessing the relative importance of each criterion, while the Combine Compromise Solution allows us to reach a compromise solution that blends different preferences and criteria. The result is recommendations that are more accurate and tailored to potential guests' needs and preferences. The recommendation results showed that AN Hotel with a value of 1,782 got 1st place, AL Hotel with 1.271 got 2nd place, and YN Hotel with 1,145 got 3rd rank.
Decision Support System for Optimizing Supplier Selection Using TOPSIS and Entropy Weighting Methods Yudhistira, Aditia; Wang, Junhai; Rahmanto, Yuri; Setiawansyah, Setiawansyah
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 5 (2024): JPTI - Mei 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.456

Abstract

Supplier selection is a crucial process in supply chain management, where companies must determine the best suppliers who are able to meet their needs based on various criteria. Companies often face challenges in managing the various factors that influence supplier selection decisions, suppliers that offer low prices may not always provide the best quality or consistent delivery times. Optimizing supplier selection through the DSS approach, companies can build stronger relationships with high-performing suppliers, while improving overall business resilience and competitiveness. The combination of the TOPSIS method and entropy weighting in supplier selection optimization provides a robust approach to evaluating and selecting the best suppliers based on predetermined criteria. This combination not only improves objectivity and accuracy in the evaluation process, but also allows decision-makers to consider trade-offs between various criteria more effectively. The purpose of the research of the combination of the TOPSIS method and entropy weighting in optimizing supplier selection is to produce objective and data-based criteria weighting through the application of the entropy weighting method, thereby reducing subjectivity in the supplier selection process. The results of the preference value calculated using the TOPSIS method resulted in the first rank with the highest preference value of 0.78393, followed by GH Supplier with a value of 0.75611, and FR Supplier in third place with a value of 0.6913. The next supplier is Supplier AG with a value of 0.59912, followed by Supplier BR with 0.51682, and Supplier TR in sixth position with 0.465. Supplier IH has a preference value of 0.43166, followed by Supplier YS with a value of 0.3984, and finally Supplier RT is in the lowest position with a value of 0.35517. This ranking shows that US Supplier is the best supplier, while Supplier RT is the lowest choice based on the criteria used.
Penerapan Root Assessment Method dan Pembobotan ROC untuk Evaluasi Kinerja Tim Penjualan Yudhistira, Aditia; Wahyudi, Agung Deni; Nuryaman, Yosep
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 2 (2024): Volume 3 Number 2 September 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i2.30

Abstract

The performance of the sales team is one of the important indicators of a company's success, which is measured through the achievement of sales targets, negotiation skills, and efficiency in managing customer relationships. Problems in the appraisal of sales team performance often arise due to various factors that affect objectivity and fairness in evaluation. One of the main problems is the lack of consistent assessment standards, where each team member may be assessed with different assessment opinions, leading to bias in evaluation results. The application of the Root Assessment Method and ROC Weighting in the performance evaluation of the sales team is an innovative and effective approach to improve productivity and team performance. The combination of these two methods not only facilitates in-depth analysis of individual and team performance, but also supports strategic decision-making in the continuous development and improvement of sales team capabilities. The results of the sales team's performance evaluation assessment ranking, Team J ranked first with a final score of 4.3981, showing the most superior performance among the entire team. In second place, Team G obtained a score of 4.388, followed by Team D in third place with a score of 4.3752. These results provide a comprehensive picture of each team's performance rankings, with Team J showing top performance, while Team F needs further improvement.
Sistem Pendukung Keputusan untuk Evaluasi Kinerja Menggunakan Metode TOPSIS: Studi Kasus Penilaian Karyawan Yudhistira, Aditia; Widodo, Tri
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 2 No. 3 (2024): Volume 2 Number 3 July 2024
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v2i3.145

Abstract

Employee performance evaluation is a systematic process carried out by an organization to assess the contribution, ability, and achievement of individuals in carrying out their duties and responsibilities. This process is not only aimed at identifying the best performers, but also to uncover potential employee development as well as areas for improvement. Employee performance evaluations often face challenges in ensuring the objectivity of the assessment, especially when relying on the subjective perception of the appraiser. Traditional methods often rely on the subjectivity of the assessor, which can result in less accurate or unfair evaluations. In addition, in organizations that have many employees with diverse backgrounds and tasks, consistent and comprehensive assessments are becoming increasingly difficult. The purpose of this study is to implement SPK based on the TOPSIS method to evaluate employee performance objectively and systematically, as well as to increase transparency and consistency in the employee performance evaluation process. The ranking results show the ranking of employee performance evaluation results based on the scores obtained by each candidate. Candidate AF ranked first with the highest score of 0.8471, followed by Candidate SR with a score of 0.7055 got second place. The third position was occupied by HA Candidate with a score of 0.4975. The results of this ranking provide an overview of each candidate's overall performance.
Penerapan Metode Simple Additive Weighting dan Pembobotan Entropy Untuk Penentuan Teknisi Terbaik Yudhistira, Aditia
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 2 No. 3 (2024): Volume 2 Number 3 September 2024
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v2i3.133

Abstract

Determining the best technicians in a company is often a challenge due to a variety of factors that need to be considered. Each technician has different advantages and disadvantages, making objective performance measurement difficult. This problem is often exacerbated by the subjectivity of assessments, especially if the evaluation is based solely on the subjective assessment of the supervisor or management team without considering in-depth performance data. The application of the SAW method with entropy weighting for the determination of the best technician is an approach that combines a simple calculation process with objective criterion weighting, based on the degree of variability of data between technicians. The combined application of SAW and entropy provides a fair, objective, assessment system in determining the best technicians, which is able to help companies to identify the technicians with the most superior performance. The results of the analysis of the selection of the best technician using the SAW method show that Technician E is ranked at the top with a final score of 0.8619, making it the best choice based on the criteria that have been assessed. The next ranking was filled by Technician A with a score of 0.8381, and Technician C who obtained a score of 0.8310, showing their excellent performance.
Pengaruh Video Unboxing TikTok pada Keputusan Pembeli Produk Kecantikan Generasi Z: Pendekatan Kuantitatif dan Kualitatif Sanjaya, M. Alung; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.566

Abstract

Video unboxing di TikTok menjadi suatu tren yang dapat mempengaruhi perilaku konsumen, khususnya pada generasi Z. Penelitian ini menganalisis pengaruh video unboxing di TikTok terhadap keputusan pembelian produk kecantikan oleh generasi Z. Metode kuantitatif menggunakan kuesioner daring dengan 100 responden (50 laki-laki dan 50 perempuan), sedangkan metode kualitatif menggunakan wawancara mendalam dengan 15 responden. Hasil menunjukkan bahwa video unboxing meningkatkan keyakinan terhadap kualitas dan keaslian produk serta lebih dipercaya dibandingkan iklan tradisional. Selain itu, efek FOMO (fear of missing out) yang diciptakan mendorong keputusan pembelian lebih cepat. Dari penelitian ini memberikan dampak wawasan yang bermanfaat bagi pemasar untuk memanfaatkan video unboxing sebagai strategi pemasaran yang efektif menjangkau generasi Z di bidang kecantikan.
Analisis Sentimen Subsidi Kendaraan Listrik di Aplikasi X menggunakan Support Vector Machine Anggoro, Bayu; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.589

Abstract

Kebijakan subsidi kendaraan listrik memicu berbagai tanggapan di masyarakat yang menjadi topik diskusi hangat di media sosial, termasuk aplikasi X. Masalah utama yang dihadapi adalah beragamnya opini masyarakat, dari yang mendukung kendaraan listrik sebagai solusi defisit migas hingga yang menyoroti perlunya persiapan matang, khususnya infrastruktur. Penelitian ini bertujuan untuk mengidentifikasi kecenderungan opini masyarakat (positif, negatif, atau netral) terkait kebijakan tersebut. Penelitian menggunakan metode algoritma Support Vector Machine (SVM) yang dikenal unggul dalam kinerja klasifikasi, penanganan ketidakseimbangan data, dan data berdimensi tinggi. Dataset terdiri dari 1.812 tweet yang, setelah melalui tahapan preprocessing, dibagi menjadi 1.449 data latih dan 363 data uji. Hasil analisis menunjukkan bahwa 87,9% (319 tweet) bersentimen netral, 5,2% (19 tweet) negatif, dan 6,9% (25 tweet) positif, menandakan masyarakat belum memiliki pandangan tegas terhadap kebijakan ini. Metode SVM menghasilkan performa yang baik dengan akurasi 86,43%, precision positif 83,33%, recall 87,30%, dan f1-score 85,27%. Dampak penelitian ini diharapkan memberikan wawasan mendalam tentang persepsi masyarakat terhadap subsidi kendaraan listrik sehingga dapat mendukung pengambilan kebijakan yang lebih efektif dan tepat sasaran.
Analisis Sentimen Media Sosial Terhadap Calon Pilkada 2024 Dengan Metode Naïve Bayes Fitrianti, Suci; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.610

Abstract

Menjelang Pilkada Indonesia 2024, polarisasi politik dan sentimen masyarakat menjadi isu penting yang dianalisis melalui media sosial. Penelitian ini bertujuan untuk mengevaluasi metode Naïve Bayes dalam klasifikasi sentimen otomatis terhadap opini publik. Sebanyak 6.465 tweet dianalisis, terdiri dari 4.169 tweet positif dan 2.296 tweet negatif. Data diproses melalui tahapan preprocessing seperti pembersihan teks, tokenisasi, dan normalisasi. Klasifikasi dilakukan menggunakan tiga varian Naïve Bayes yaitu MultinomialNB, GaussianNB, dan BernoulliNB. Hasil menunjukkan bahwa MultinomialNB memiliki performa terbaik dengan akurasi 75%, recall 95%, dan F1-Score 84%, sangat efektif dalam mendeteksi sentimen positif. BernoulliNB mencatat akurasi 74% dengan F1-Score 81% untuk sentimen positif, meskipun performa pada sentimen negatif lebih rendah yaitu F1-Score 62%. Sebaliknya, GaussianNB menunjukkan performa terendah dengan akurasi 56%, yang kurang optimal untuk data teks diskrit. Dominasi data positif memengaruhi performa model, membuatnya lebih akurat pada kelas mayoritas. Penelitian ini menunjukkan potensi metode Naïve Bayes, khususnya MultinomialNB, untuk memantau opini publik secara real-time selama pemilu, sekaligus menjadi dasar pengembangan analisis sentimen berbasis data yang lebih baik.
Analisis Sentimen Terhadap Cyberbullying pada Platform Media Sosial X Menggunakan Algoritma Naive Bayes Mahmudah, Siti Anisa; Yudhistira, Aditia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.628

Abstract

Perundungan telah menjadi isu global yang tidak lagi terbatas oleh batasan geografis, tetapi juga merambah ke media sosial, sehingga menciptakan tantangan baru dalam upaya pencegahan dan penanganannya. Penelitian ini bertujuan untuk mengevaluasi sentimen terkait cyberbullying dalam komunitas online dengan menerapkan algoritma Naive Bayes, menggunakan metode MultinomialNB dan BernoulliNB. Data dikumpulkan melalui proses crawling dari platform media sosial X, kemudian dianalisis melalui tahapan preprocessing yang terstruktur. Kinerja model dinilai menggunakan metrik seperti akurasi, presisi, recall, dan F1-score. Berdasarkan hasil analisis, metode BernoulliNB menunjukkan keunggulan dalam mengidentifikasi sentimen positif dengan presisi sebesar 68,44% dan recall 39,15%, sedangkan MultinomialNB lebih efektif dalam mendeteksi sentimen negatif. Namun, kedua model menghadapi tantangan dalam mengatasi ketidakseimbangan data yang memengaruhi performa keseluruhan. Penelitian ini berkontribusi dalam menyediakan metode analisis sentimen berbasis algoritma probabilistik yang efisien untuk mendeteksi pola-pola cyberbullying secara otomatis. Hasil yang diperoleh dapat dimanfaatkan untuk mengembangkan sistem deteksi cyberbullying yang lebih andal, memberikan solusi praktis bagi platform media sosial dalam menciptakan lingkungan digital yang lebih aman dan ramah bagi penggunanya.
Co-Authors Adamansyah, Eka Putri Ahmad Ari Aldino Anggoro, Bayu Ari Sulistiyawati Arshad, Muhammad Waqas Chandra, Iryanto Dedi Darwis Gustina, Intan Hafizhah, Hafizhah Handika, Tegar Dio Luthfi Falih, Yudep Rafidal Ma'rufudin Mahmudah, Siti Anisa Megawaty, Dyah Ayu Nasrul, Royhan niswatun umami, Nila Nuryaman, Yosep Palupiningsih, Pritasari Pasaribu, A. Ferico Octaviansyah Ryan Randy Suryono 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 Yuri Rahmanto