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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM Journal of Artificial Intelligence and Digital Business
Azahari, Azahari
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Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health

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SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KINERJA GURU PADA SMA NEGERI 1 LONG IKIS MENGGUNAKAN METODE PREFERENCE RANKING ORGANIZATION METHOD FOR ENRICHMENT EVALUATION (PROMETHEE) azahari, azahari; Ukkas, Irwan; Ar Rahman, Nurbalingga Suhendra
JURNAL IT Vol 10 No 2 (2019): Volume 10 Nomor 2, Agustus 2019 Jurnal IT
Publisher : LPPM- STMIK HANDAYANI MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37639/jti.v10i2.153

Abstract

Penilaian kinerja guru adalah penilaian yang dilakukan pada setiap item tugas utama guru dalam konteks pengembangan karir, pangkat, dan posisi. Implementasi tugas utama guru tidak lepas dari kemampuan seorang guru dalam menguasai dan menerapkan kompetensinya. guru sebagaimana diamanatkan oleh Peraturan Menteri Pendidikan Nasional Nomor 16 Tahun 2007 tentang Standar Kualifikasi Akademik dan Kompetensi Guru. PROMETHEE adalah penggunaan nilai-nilai dalam hubungan peringkat luar. Dalam metode ini, semua parameter yang dinyatakan memiliki pengaruh sesuai dengan prospek ekonomi. Metode PROMETHEE menggunakan kriteria dan nilai masing-masing. Sistem Pendukung Keputusan dengan menggunakan metode PROMETHEE diharapkan dapat membantu sekolah dalam menilai kinerja guru, sehingga tingkat kinerja guru dapat mempengaruhi kualitas sekolah sambil berguna untuk pengembangan guru secara optimal diperlukan untuk memajukan kualitas pendidikan.
Penerapan Metode Topsis Pada Sistem Pendukung Keputusan Kelayakan Penerima Dana Bantuan Operasional Sekolah Azahari, Azahari; Pahrudin, Pajar; Yunita, Yunita
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2290

Abstract

One of the ways used to fulfill education. The Indonesian government implements a 12-year compulsory education program. Although there is a 12-year compulsory education program by the government, there are still some students who cannot continue their education due to factors from the family economy who are unable to meet the needs or costs of the education they take. The School Operational Assistance Fund (BOS) is a financial aid given to underprivileged students/I to be able to meet learning needs such as tuition fees, book fees or personal needs that support the implementation of education for students/I. For private schools, the School Operational Assistance Fund (BOS) has its own quota to be given to students. The organizing committee for the recipients of the School Operational Assistance Fund (BOS) is required to be fair and honest in the selection process. The error is because there is still no special provision used for the selection process or the assessment process carried out by the school. Decision Support System (DSS) is a system that has been integrated with a computer, where the decision support system is used to provide certain provisions that can be used to assist in providing recommendations in the decision-making process. TOPSIS uses the principle that the chosen alternative must have the closest distance from the positive ideal solution and the farthest from the negative ideal solution from a geometric point of view by using Euclidean distance to determine the relative proximity of an alternative to the optimal solution. By applying the TOPSIS method, Alternative 4 (A4) was selected as the beneficiary with a final score of 0.7251
Prediksi Persediaan Bahan Baku Makanan Menerapkan Algoritma Apriori Data Mining Salmon, Salmon; Azahari, Azahari; Yusnita, Amelia
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2563

Abstract

The company's operational activities are inseparable from the supply of raw materials that must be met every day to meet consumer demand. The restaurant uses raw materials, namely vegetables, raw meat which includes beef and chicken, yellow noodles and soun noodles, and the main seasoning. Sales of food at this restaurant quite a lot in a day. This will produce sales data that will continue to grow every day, but this data is useless if it is not processed again to get the knowledge contained in the data. The Apriori algorithm is a method for finding patterns of relationships between one or more items from a dataset. Thus the pile of data that has been collected can produce a sales pattern, from which the customer's buying interest in food can be identified. From the results of research using a data sample of 18 items with a minimum of 20% Support and 50% Confidence, it produces 5 interesting rules with the highest Support reaching 33.33% and the highest Confidence reaching 100%.
Optimisasi Kompetensi Mahasiswa Dalam Analisis dan Perancangan Sistem Informasi Harianto, Kusno; Azahari, Azahari; Yusnita, Amelia
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 7, No 3 (2024): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v7i3.6801

Abstract

Pelatihan Analisis dan Perancangan Sistem Informasi telah diselenggarakan bagi Mahasiswa STMIK Widya Cipta Dharma. Terdapat kesenjangan antara teori yang diajarkan di kelas dan keterampilan praktis yang dibutuhkan di dunia kerja, yang menyebabkan banyak mahasiswa merasa kurang siap menghadapi tantangan nyata. Oleh karena itu, pelatihan yang difokuskan pada analisis dan perancangan sistem informasi diharapkan dapat mengatasi kesenjangan dengan memberikan pengalaman langsung dengan tujuan mempersiapkan mahasiswa STMIK Widya Cipta Dharma agar lebih kompetitif di dunia kerja. Pelatihan ini menerapkan motode yang terdiri dari 3 (tiga) tahapan yaitu : persiapan, pelaksanaan, dan evaluasi. Pada tahap persiapan Unit PIKP STMIK Widya Cipta Dharma yang bertanggung jawab penuh untuk mengkoordinasikan proses pendaftaran dan penyebaran informasi. Pada tahap pelaksanaan materi yang disampaikan selama pelatihan berfokus pada penggunaan Unified Modeling Language (UML) dengan aplikasi Rational Rose. Peserta pelatihan, yang terdiri dari 8 mahasiswa semester akhir, diwajibkan untuk menyelesaikan dan mempraktekkan langsung membuat perancangan sistem sesuai dengan contoh kasus dan final project yang diberikan. Hasil evaluasi dinilai dari aspek kehadiran 30%, submisi tugas pertemuan 30% dan final project 40%. Rata-rata nilai yang diperoleh mahasiswa dari hasil evaluasi adalah 84.5 dengan keterangan baik sekali. Kendala yang dihadapi selama pelatihan adalah keterbatasan ruangan yang kurang mampu menampung peserta yang cukup banyak. Secara keseluruhan, pelatihan ini bertujuan untuk membekali mahasiswa dengan keterampilan dan kompetensi dalam menganalisis dan merancang sistem informasi yang dibutuhkan di dunia industri kerja.
Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Algoritma Random Forest Untuk Klasifikasi Data Mining Pada Penyakit Gagal Ginjal Salmon, Salmon; Azahari, Azahari; Ekawati, Hanifah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6476

Abstract

Kidney failure is one of the most common chronic diseases worldwide. This condition occurs when the kidneys lose their ability to filter waste and excess fluid from the blood. Kidney failure is a serious condition that occurs when kidney function decreases significantly or stops altogether. Kidney failure has a wide impact on the physical, mental, and social health of patients. Therefore, early treatment and a holistic approach are needed to minimize its impact. In the health sector, technological advances have enabled more effective processing of medical data through the application of data mining. Data Mining is the process of exploring and analyzing large amounts of data to find patterns, relationships, or valuable information that was previously unknown. Classification in Data Mining is the process of grouping or categorizing data into certain classes or labels based on the attributes or features it has. In the classification itself, there are various algorithms in it such as the K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms are two algorithms that are widely used in classification tasks. Therefore, this study will carry out a comparison process on the performance of the K-Nearest Neighbor algorithm and the Random Forest algorithm. Comparison of data mining algorithm performance to evaluate and determine which algorithm is the most effective and efficient in solving a particular problem based on various evaluation metrics. Overall, the accuracy value obtained is above 90%, but the Random Forest algorithm has better performance. Where the accuracy level results obtained from the Random Forest algorithm are 99.75%. Therefore, the model or pattern produced by the Random Forest algorithm will later be used to assist in the process of diagnosing kidney failure and the Random Forest algorithm is an algorithm that has better performance.
Application of K-Means Algorithm for Segmentation Analysis of Youtube Viewers in Indonesia Halim, Ryan Artanto; Pratiwi, Heny; Azahari, Azahari
INFOKUM Vol. 13 No. 03 (2025): Infokum
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v13i03.2850

Abstract

The application of K-Means as a clustering method in segmentation analysis is common. However, academic research on YouTube audience segmentation in Indonesia is still limited. YouTube audiences in Indonesia are diverse, ranging from entertainment, education, to news, so more in-depth analysis is needed to identify user segments more specifically. YouTube audience segmentation can provide a deeper understanding of people's video consumption behavior. This understanding can help content creators and digital industry players develop more effective content strategies. K-Means was chosen as the clustering method in this study because it can group YouTube viewers in Indonesia based on their interaction patterns with YouTube content. In addition, K-Means' ability to handle large data is suitable for segmenting platforms with a large number of users such as YouTube. This research uses three main features, namely views, duration, and engagement rate to group viewers into five clusters. Cluster evaluation using Silhouette Score (0.3445), Davies-Bouldin Index (0.9576), and Calinski-Harabasz Index (481.4730) shows that the resulting segmentation is of good quality. The analysis shows that there are differences in video consumption patterns across clusters, reflecting variations in viewer preferences and engagement levels.
Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1 Azahari, Azahari; Yulindawati, Yulindawati; Rosita, Dewi; Mallala, Syamsuddin
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020732093

Abstract

Prediksi  kelulusan  dibutuhkan  oleh  manajemen  perguruan  tinggi  dalam  menentukan kebijakan  preventif  terkait  pencegahan  dini  kasus drop  out. Lama masa studi setiap mahasiswa bisa disebabkan dengan berbagai faktor.  Dengan  menggunakan data mining algoritma naive bayes dan neural network dapat  dilakukan  prediksi  kelulusan  mahasiswa di  STMIK  Widya  Cipta  Dharma (WiCiDa) Samarinda . Atribut yang digunakan yaitu, umur saat masuk kuliah, klasifikasi kota asal Sekolah Menengah Atas, pekerjaan ayah, program studi, kelas, jumlah saudara, dan Indeks Prestasi Kumulatif (IPK). Sampel mahasiswa yang lulus dan drop-out pada tahun 2011 sampai 2019 dijadikan sebagai data training dan data testing. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data training, 321 sebagai data testing, dan 1139 sebagai data target. Semua data diambil dari data mahasiswa program strata 1, dan tidak mengikut sertakan data mahasiswa D3 dan alih jenjang/transfer.  Dari data testing diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi naive bayes dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi neural network adalah 72,58%, sehingga metode alternatif inilah yang lebih baik. Proses evaluasi dan analisis dilakukan untuk melihat dimana letak kesalahan dan kebenaran dalam hasil prediksi masa studi.AbstractGraduation predictions are required by the higher education institution preventive policies related to the early prevention of drop-out cases. The duration of study, for each student can be caused by various factors. By using the data mining algorithm Naive bayes and neural network, the student graduation in STMIK Widya Cipta Dharma (WiCiDa) can be predicted. The attributes used are as follows: age at admission, classification of cities from high school, father’s occupation, study program, class, number of siblings, and grade point average (GPA). Samples of students who graduated and dropped out between year 2011 and 2019 were used as training data and testing data. While the year class of 2015to 2018 is used as the target data, which will be predicted during the study period. According to the data mining algorithm Naive bayes, there are 3229 students; 1769 as training data, 321 as testing data, and 1139 as target data. All data is taken from students enrolled in undergraduate program and does not include data on diploma students and transfer student. From the testing data, an accuracy rate only 57.63%. The other side, prediction accuracy of the neural network is 72.58%, so this alternative method is the best chosen. The research results show the many weaknesses of the results of prediction of Naive bayes because the level of accuracy of its validity is not high. The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions.
Determining the Country with the Best Economic Conditions 2025 using the MCDM Method Harpad, Bartolomius; Azahari, Azahari; Salmon, Salmon
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7346

Abstract

In the midst of increasingly complex global challenges in 2025, evaluating a country's economic condition is an important element in supporting strategic decision-making, whether at the government, corporate or individual level. The diversity of economic indicators such as Gross Domestic Product (GDP), inflation, unemployment, and human development index often makes it difficult to make an objective and comprehensive assessment. Reliance on a single indicator tends to produce a biased and unrepresentative picture. To address these issues, this research adopts a Multi-Criteria Decision Making (MCDM) approach that is able to consider various economic aspects simultaneously and systematically. The three MCDM methods used in this study are TOPSIS, VIKOR, and COCOSO. The analysis was conducted on 19 countries using four main indicators, namely GDP in billion USD, inflation rate, unemployment rate, and economic growth rate. Based on the results of data processing, the USA occupies the top position as the country with the best economic performance, followed by China. The three methods show consistency in ranking some countries, but there are also striking differences for some alternatives due to different approaches in normalisation and weighting. These findings emphasise the importance of choosing the right method in multicriteria evaluation. Therefore, a combined approach such as ensemble decision-making is recommended to strengthen the validity of the results. For further development, the use of additional indicators and the integration of artificial intelligence-based technology are suggested to improve accuracy and flexibility in analysing economic conditions between countries.
Sistem Pengukur Kondisi Fisik Atlet Tarung Derajat Berbasis Web Sari, Aulia Rahmita; Azahari, Azahari; Harianto, Kusno
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3447

Abstract

Dalam dunia olahraga prestasi, kemampuan fisik merupakan faktor utama yang menentukan keberhasilan seorang atlet dalam mencapai performa optimal. Salah satu aspek penting dalam pembinaan atlet adalah pemantauan dan pengukuran kondisi fisik secara berkala untuk mengetahui tingkat kebugaran dan kesiapan atlet dalam menghadapi pertandingan. Bertujuan mengimplementasikan aplikasi pengukur kondisi fisik atlet Tarung Derajat berbasis web agar membantu pelatih dan atlet mengelola data hasil tes fisik secara lengkap. Aplikasi ini dikembangkan menggunakan metode Waterfall pengembangan perangkat lunak yang menggunakan pendekatan linear dan berurutan, cocok untuk proyek dengan kebutuhan yang stabil sejak awal dan juga cocok untuk proyek yang mengedepankan kualitas. pengujian black box mungkinkah perekayasan perangkat lunak mendapatkan serangkai kondisi input yang sepenuhnya menggunakan semua persayaratan fungsional untuk suatu program menunjukkan aplikasi mampu menampilkan hasil pengukuran kondisi fisik atlet secara akurat, menyediakan laporan perkembangan performa secara otomatis, serta memudahkan pelatih dalam melakukan evaluasi. Dengan adanya aplikasi ini, proses penilaian kondisi fisik atlet menjadi lebih terstruktur, cepat, dan terdokumentasi dengan baik, Pelatih memperoleh informasi dan melakukan pengukuran kondisi fisik setiap Atlet tarung drajat tanpa perlu melakukan perhitungan secara manual satu persatu. Atlet tarung derajat sendiri dapat mengetahui atau memantau secara realtime hasil perhitungan kondisi fisik terakhir mereka pada Aplikasi Pengukur Kondisi Fisik Atlet Tarung Derajat.
Co-Authors Amelia Yusnita Ar Rahman, Nurbalingga Suhendra Ekawati, Hanifah Halim, Ryan Artanto Harianto, Kusno Harpad, Bartolomius Pahrudin, Pajar Pratiwi, Heny Rosita, Dewi Salmon Salmon Salmon Salmon, Salmon Sari, Aulia Rahmita Syamsuddin Mallala Ukkas, Irwan Yulindawati, Yulindawati Yunita Yunita