<p class="Abstrak">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 <em>data mining</em> algoritma <em>naive bayes</em> dan <em>neural network</em> 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 <em>drop-out</em> pada tahun 2011 sampai 2019 dijadikan sebagai data <em>training</em> dan data <em>testing</em>. Sedangkan angkatan 2015–2018 digunakan sebagai data target yang akan diprediksi masa studinya. Sebanyak 3229 mahasiswa, 1769 sebagai data <em>training</em>, 321 sebagai data <em>testing</em>, 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 <em>testing </em>diperoleh tingkat akurasi hanya 57,63%. Hasil penelitian menunjukkan banyaknya kelemahan dari hasil prediksi <em>naive bayes</em> dikarenakan tingkat akurasi kevalidannya tergolong tidak terlalu tinggi. Sedangkan akurasi prediksi <em>neural network</em> 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.</p><div><div><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Graduation 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.</em></p><p><em><strong><br /></strong></em></p></div></div>

The foundation scholarship path is a facility provided by the private sector to help government education programs realize 12 years of compulsory education, one of which is a three-year vocational high school education facility and of course the foundation will take into account who deserves three years of learning opportunities for students. Now this will help many parties who really deserve the opportunity to learn to foster their enthusiasm for learning again, but in this case also the foundation must certainly select candidates who apply for the scholarship, of course there are many who want to get the scholarship so that correct, accurate considerations must be made so that the scholarship does not fall into the wrong hands, one of the right ways is to classify the incoming data using the C4.5 algorithm application so that the canteen selection process does not It takes a very long time and based on research, the final result of the C4.5 algorithm will form a decision tree to simplify the process of reading the result data and it is hoped that this will help the foundation in the selection of foundation scholarship recipients for new students. and the results obtained are 57% nominated students have the opportunity to receive scholarships provided by SMK TI Arilangga.

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

Development of the game towards a tourist promotion tool is very important. The game "Awang exploring the City of Tenggarong" is an android smartphone game that tells the story of a tourist / traveler named Awang who roams every tourist destination in the Tenggarong City. The development of this game uses multimedia development life cycle, starting from the concept, assembly, and testing. Artificial intelligence is also needed in the development of this game. Players will be accompanied by NPC (Non Player Character) in the form of a female tour guide. NPCs equipped with the Finite State Machine (FSM) model can guide players to go to each Tenggarong tourist attraction, and it can also provide information related to these tourist attractions. The final result of this research is a game that can be a media for tourism promotion in Tenggarong city

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%.