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All Journal Dinamika Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Komtika (Komputasi dan Informatika) Informasi Interaktif Respati Techno
Sudarmawan, Sudarmawan
UNiversitas AMIKOM Yogyakarta
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Implementasi Decision Tree Algoritma C4.5 Untuk Memprediksi Kesuksesan Pendidikan Karakter Abdillah, M A; Setyanto, Arief; Sudarmawan, Sudarmawan
Jurnal Teknologi Informasi RESPATI Vol 15, No 2 (2020)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/jtir.v15i2.349

Abstract

INTISARIPencanangan kurikulum Pendidikan Karakter dalam sistem pendidikan di Indonesia, adalah sesuatu hal yang baru dan belum banyak dikaji dalam penelitian Educational Data Mining (EDM). Sebagian besar penelitian dalam ranah EDM masih menggunakan faktor kognitif dalam penilaiannya, berbeda dengan pendidikan karakter yang lebih berorientasi kepada pengajaran nilai-nilai karakter, serta mempertimbangkan latar belakang peserta didik. Oleh karena itu, diperlukan cara atau metode untuk mengidentifikasi calon peserta didik, serta memprediksi kesuksesannya dalam sistem pendidikan karakter. Algoritma C4.5 dapat digunakan untuk melakukan prediksi dan klasifikasi terhadap calon siswa dengan cara membuat pohon keputusan berdasarkan data-data yang sudah ada dan melakukan prediksi terhadap calon siswa baru, dalam penelitian ini peneliti menggunakan data mahasiswa Unires Yogyakarta sebagai objek penelitian. Dengan penelitian ini, juga diharapkan dapat diketahui tingkat akurasi Decision Tree Algoritma C.45 dalam mengukur pengaruh atribut-atribut latar belakang siswa tersebut terhadap kesuksesan pendidikan karakter, sehingga akan diketahui apakah Decision Tree Algoritma C.45 memenuhi aspek reliabilitas dan validitas sebagai alat ukur kesuksesan pendidikan karakter. Dari hasil pengukuran, diketahui bahwa kombinasi atribut Bidang Bahasa dan Sosial, Latar belakang pendidikan agama dan orang tua yang menjadi seorang pendidik/guru, serta kemampuan untuk membaca Al-Qur’an berkorelasi positif terhadap kesuksesan pendidikan karakter. Nilai accuracy sebesar 60,91%, menunjukkan bahwa algoritma  decision tree C4.5 layak digunakan untuk melakukan prediksi tingkat kesuksesan pada pendidikan karakter. Namun masih butuh banyak kajian mendalam terutama dalam menentukan atribut-atribut yang memang benar-benar mempengaruhi kesuksesan pendidikan karakter sehingga diharapkan akan didapat accuracy yang lebih baik, serta terjadi efisiensi dalam pengelompokan atribut. Kata kunci— data mining, klasifikasi, Decision Tree ,Algoritma C4.5, prediksi kelulusan, pendidikan karakter. ABSTRACTThe launching of the Character Education curriculum in the education system in Indonesia, is something new and has not been much studied in Educational Data Mining (EDM) research. Most of the research in the realm of EDM still uses cognitive factors in its assessment, in contrast to character education which is more oriented towards teaching character values, and taking into account the background of students. Therefore, we need a way or method to identify potential students, and predict their success in the character education system. C4.5 algorithm can be used to make predictions and classifications of prospective students by making a decision tree based on existing data and predicting new prospective students, in this study the researchers used the data of Yogyakarta Unires students as research objects. With this research, it is also expected to know the accuracy of the Decision Tree Algorithm C.45 in measuring the influence of the background attributes of these students on the success of character education, so it will be known whether the Decision Tree C.45 algorithm meets the aspects of reliability and validity as a measure of success character building. From the measurement results, it is known that the combination of attributes of Language and Social Affairs, the background of religious education and parents who become educators/teachers, and the ability to read Al-Qur'an are positively correlated to the success of character education. Accuracy value of 60.91%, shows that the decision tree C4.5 algorithm is feasible to uses to predict success rates in character education. But it still needs a lot of in-depth study, especially in determining the attributes that really affect the success of character education so that it is expected to get better accuracy, and efficiency in the grouping of attributes.Keywords - data mining, classification, Decision Tree, C4.5 algorithm5, graduation prediction, character  education.
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes Nurmalasari, Maulidya Dwi; Kusrini, Kusrini; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5140

Abstract

Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.
Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika Rahman, Arif; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5188

Abstract

Sentiment analysis is a science to extract text to get someone's emotions for that. The benefits of sentiment analysis have many benefits, one of which is to see whether or not customers have a good response to the product and this can be an input for the development of the product's business in the future. The weakness of previous studies in research sentiment analysis is that the authors conduct research to improve the results of previous studies using the naïve Bayes algorithm that is optimized with a genetic algorithm. From the results of the research that has been done, the average value in this study is on average better than previous studies, no applications have been identified as underfitting or overfitting and finally the naïve Bayes algorithm that has been optimized by the genetic algorithm can be a classification solution for sentiment analysis.
Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes Setiawan, Hendrik; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5189

Abstract

The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm
Evaluasi Tingkat Usability Website KPPN Kabupaten ABC Menggunakan Prinsip Usability Putra, Afrizal Yudano Perdana; W A, Bambang Soedijono; Sudarmawan, Sudarmawan
Jurnal Teknologi Informasi RESPATI Vol 14, No 2 (2019)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/jtir.v14i2.293

Abstract

INTISARIWebsite merupakan syarat satu alternatif bagi lembaga keuangan khususnya KPPN untuk mengenalkan profil serta memberi informasi terbaru kepada para penggunaannya. Karena pentingnya website sebagai sarana informasi dalam perkembangan teknologi, maka dibutuhkan evaluasi kualitas website agar proses pelayanannya tetap berjalan lancar. Namun, Belum terdapat standard khusus dan belum pernah dilakukannya evaluasi pada website KPPN. Usability adalah sebuah metode yang digunakan untuk menguji kebergunaan website dan mengetahui sejauh mana kebergunaan website tersebut. Evalusi website KPPN menggunakan prinsip usability yang akan digabungkan dengan metode system usability scale (SUS) ini diharapkan mendapatkan hasil yang baik. Jenis penelitian ini adalah penelitian deskriptif dengan pendekatan kuantitatif. Metode pengumpulan data yang digunakan yaitu wawancara dan kuesioner. Metode analisis data digambarkan dengan hubungan data yang diambil menggunakan skala likert. Sehingga hasil data yang diperoleh bisa dianalisis menggunakan metode usabilty dan mamasukkan dalam lima atribut usability. Hasil yang diperoleh dari penelitian ini adalah website KPPN Kabupaten ABC memperoleh hasil sebebsar 79% dan termasuk dalam kategori baik. Namun perlu adanya perbaikan pada webiste agar pengguna mendapat kepuasan yang maksimal..Kata Kunci— Evaluasi, Website, Usability, Atribut, Metode.  ABSTRACTThe website is a requirement of the alternatives for financial institutions, especially the KPPN to introduce the profile and provide updated information to the user. Because of the importance of the website as a means of information in the development of technology, it is necessary to allow the evaluation of the quality of website services running smoothly. However, yet there are special standards and had never done an evaluation on the website of the KPPN. Usability is a method used to test the usefulness of the website and determine the extent of the usefulness of the website. Evaluation KPPN websites using usability principles that will be combined with the method of system usability scale (SUS) is expected to get a good result. This type of research is descriptive research with quantitative approach. Data collection methods used were interviews and questionnaires. The data analysis method described by relationship data taken using a Likert scale. So that the data obtained can be analyzed using methods usabilty and inclusion in the five attributes of usability. Results obtained from this study is the KPPN website ABC obtain the results isr 79% and included in both categories. But the need for improvement on web pages so that users get the maximum satisfaction.Keywords— Evaluation, Website, Usability, Atribut, Method.
Prediksi Kelulusan Tepat Waktu Menggunakan Metode C4.5 DAN K-NN (Studi Kasus : Mahasiswa Program Studi S1 Ilmu Farmasi, Fakultas Farmasi, Universitas Muhammadiyah Purwokerto) Eko Purwanto; Kusrini Kusrini; Sudarmawan Sudarmawan
Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Vol 20, No 2 (2019): Techno Volume 20 No.2 Oktober 2019
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/techno.v20i2.5160

Abstract

The graduation profile is one of the key elements for the accreditation standard of higher education. It mirrors the performance of the applied educational system within a period of time. The better it is, the better the accreditation will be. In support of this, a graduation prediction may be conducted to the academic database of the students. It is of pivotal to trace and classify the historical data into the data training and data testing, thus, to predict the on time-graduation. The step is importantly done to help decide the better management of learning processes. This study was therefore done to analyse certain variables applied to predict the on time-graduation using the algorythms of C.45 and K-Nearest Neighbour (K-NN). The data mining was done to the academic database of the students of the Pharmacy study programme, Pharmacy Faculty, Muhammadiyah University of Purwokerto by adding certain variables into the process. The data was then classified into the data training and data testing. Backward selection was done to select the best and most influential variables for the dataset. The study further resulted that by using the algorhythm of C.45 and backward selection, the accuracy of the graduation reached 92.75%. It is different from the acurracy the K-NN and backward selection showed that reached 96.14%. The result confirmed that the KNN showed the better accuracy than the C.45. It considerably benefitted the study programme to make better decisions on increasing the quality of services, in particular that of leraning processes.
Co-Authors Abdillah, M A Abidarin Rosidi Adiwisanghagni, Mohammad Agung Mulyono Agung, Kris Ahmad Zarkasih Amrullah, Ahmad Afief Arief Setyanto Arif Rahman Arik Sofan Tohir Sofan Tohir, Arik Sofan Tohir Bambang Soedijono W A, Bambang Soedijono Eko Purwanto Ema Utami Fatkhurohman, Ari Hendrik Setiawan, Hendrik Hermanto (STMIK AMIKOM Yogyakarta) JOKO PRAYITNO Kusrini Kusrini Kusrini Kusrini Mohammad Suyanto Napianto, Riduwan Nugroho, Muhammad Agung Nurmalasari, Maulidya Dwi Putra, Afrizal Yudano Perdana Rumini - Sukasna, Sukasna Tri Yusnanto