<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>

Game simulation-based education is designed to simulate the existing problems in order to obtain knowledge that can be used to solve the problem. Simulation games with educational purposes can be used as a medium of education that has the learning patterns of learning by doing. Based on the pattern which is owned by the game, players are required to learn in order to solve existing problems. In making the educational game “Flora the Explorer Introduction to Plant with Android-Based Finite State Machine”. Applications used in making the game is Swish max4 and the shuffle algorithm random expected this game does not become monotonous, and players cannot remember the position of the bubble in the game, then the game agent using the Finite State Machine (FSM), the game agent will give notice to when the players answer wrong or right in the game

Educational game "Ajut-ajut Kids" is developed in multimedia stages to introduce the Dayak Benuaq language for children. In this matching game, players must match two or more images and words in Dayak language. This study applies randomization to shuffle position of images and words in each round. It makes the game more challenging. Random and probability in-game agents are also applied, which will accompany children to play the kid character in Dayak clothes. Game agents, which apply the Finite State Machine (FSM) model, will provide expressions like pleasure, sadness, etc., according to the child's playing style. Random implementation and credibility in the FSM model allows agents to give random expressions. This makes the agent more natural. The result of this study proves randomization and probability have succeeded make this game more interesting and interactive to children. Intisari-Edugame "Ajut-ajut Kids" dibangun melalui tahapan multimedia untuk mengenalkan pembelajaran bahasa Dayak Benuaq kepada anak. Permainan ini berjenis matching, yaitu pemain harus mencocokkan gambar dan kata bahasa Dayak. Makalah ini menerapkan teknik pengacakan pada susunan gambar dan kata pada setiap babak untuk membuat permainan tidak berkurang tantangannya. Pengacakan dan probabilitas juga diterapkan dalam agen cerdas yang menemani anak bermain dalam bentuk karakter cilik berbaju adat Dayak. Agen cerdas, yang menerapkan model Finite State Machine (FSM), akan memberikan ekspresi senang, sedih, dan sebagainya, sesuai gaya bermain anak. Adanya pengacakan dan probabilitas dalam model FSM membuat agen kadang-kadang juga dapat memberikan ekspresi yang acak. Hal ini membuat agen lebih natural. Hasil pengujian membuktikan pengacakan dan probabilitas berhasil membuat permainan ini tidak monoton dan lebih interaktif.

The purpose of this research was to obtain an excellent school management model for vocational high school. The model which was meant in this research was a school management model based on Bugis Makassar culture for vocational high school through a developmental process, by implementing the culture of 'Abbulo sibatang, sipakatau and pacce"/pesse' in the management of vocational high school through some sort of developmental process. This research is a developmental research. This research will provide a management model which applies an excellent Bugis Makassar culture (valid, practical and effective). As the consideration of efficiency, the development of those three things are conducted simultaneously. On the other hand, when the model is developed, the device management is also developed which is suitable with the model and the development of an instrument which relates to the model and device management. The development of management model in this research proceeded from the developmental modification of Plomp and Akker. There were several phases in this development, Phase 1: Preliminary investigation of management model and management device, Phase 2: Designing management model and management device, Phase 3: Realization of management model and management device, Phase 4:Testing, evaluating and revising the management device to know the validity of the management which has been designed in phase 2 and developed in detail in phase 3 according to the experts, whether it can be applied practically or not at vocational high school and to see its effectiveness in terms of purpose. In this research, there was a small-scale trial which meant it was done in the place where the research was conducted, it was SMK Farmasi Tenggarong. Based on the results, BuMa model is successful because of some indicators, such as: there are familiarity and togetherness of the school employees, included teachers and staffs, there are a lot of non-permanent teachers who wants to be the permanent teachers, and the most important one is the number of students which increases significantly from 38 to 160 students. This management model has not been tested in a greater scope due to the limitation of the researcher.

Educational game “P3K-Kids” merupakan edugame yang dibangun untuk proses pembelajaran pada anak usia 8 sampai dengan 9 tahun, edugame ini dibangun agar anak – anak dapat mengetahui bagaimana cara menangani Pertolongan Pertama Pada Kecelakaan (P3K) pada saat mereka bermain. Pada penelitian membangun edugame ini diterapkan algoroitma pengacakan posisi dengan shuffle random, dengan tujuan mengacak tantangan mengobati pasien setiap level­nya. Game dibangun menggunakan desain storyboard dan struktur hybrid dan bagan alir Unified Modeling Language (UML) dan menggunakan software SwishMax4, edugame yang dibangun dapat dijalankan pada smartphone android. Dari hasil implementasi dapat disimpulkan bahwa dengan edugame yang dapat mempermudah proses belajar dan dapat memberi pengetahuan tentang cara melakukan pertolongan pertama pada kecelakaan.