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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Teknika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Mathvision : Jurnal Matematika Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences Jurnal Abdimas Bina Bangsa JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Konferensi Nasional PKM-CSR Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Publika Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Journal of Data Science Methods and Applications
Hendra Kurniawan
Institut Informatika & Bisnis Darmajaya
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REFLEKSI ORMAWA IIB DARMAJAYA: KEARIFAN LOKAL DI KEMILING.COM Muhamad Iqbal Ardiansyah; Rizky Samjaya Putra; Ahnaf Ronaldo Nadhir; Ahmad Nur Hakim Amrullah; Elsa Agustin Marbun; Muhammad Rezky Adytama; Gusnanda Oscar; Rizki Aditya Ramadhan; Zahra Putri Assyfa; Alda Caesar Valensia; Desi Ratna Sari; Tri Melda Yama; Muhammad Sahri; Alendra Natuah Maensya; Hendra Kurniawan; Yan Aditiya Pratama
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 6 (2024): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i6.26872

Abstract

Abstrak: Pengembangan kepariwisataan berbasis teknologi digital sangat penting untuk mendukung potensi ekonomi dan pelestarian budaya di Kecamatan Kemiling, Bandarlampung. Oleh karena itu, Program Penguatan Kompetensi Organisasi Kemahasiswaan (PPK Ormawa) IIB Darmajaya bertujuan untuk meningkatkan keterampilan Digital (hardskill) dan Tata Kelola Manajemen (softskill) mitra, yaitu komunitas lokal yang terdiri dari 32 peserta Pelaku Pariwisata, UMKM, dan Pelestari Budaya. Metode yang digunakan meliputi Focus Group Discussion (FGD), pelatihan Literasi Digital dan Keamanan Siber, dan Pelatihan Tata Kelola Manajemen. Dalam mengevaluasi kegiatan ini, PPK Ormawa IIB Darmajaya menggunakan angket pre-test dan post-test, menunjukkan peningkatan skor pelatihan literasi digital dari 8.8 ke 8.9 dan pelatihan Tata Kelola Manajemen dari 8.62 ke 8.87. Program ini juga menghasilkan sistem informasi berupa website dan aplikasi mobile untuk mempromosikan potensi lokal, diharapkan dapat memperkuat ekonomi lokal serta mendukung pelestarian budaya.Abstract: The development of digital technology-based tourism is very important to support economic potential and cultural preservation in Kemiling District, Bandarlampung. Therefore, The Organizational Strengthening Program for Student Organizations (Bahasa: Program Penguatan Kompetensi Organisasi Kemahasiswaan or PPK Ormawa) IIB Darmajaya is to improve the Digital Literation skills (hard skills) and Management Governance (soft skills). This activity is local communities consisting of 32 participants (Tourism Enthusiasms, MSMEs, and Culture Enthusiasms). The methods of this activity are Focus Group Discussions (FGD), Digital Literacy and Cybersecurity training, and Management Governance Training. In evaluating this activity, PPK Ormawa IIB Darmajaya uses pre-test and post-test questionnaires. It shows an increase in digital literacy and cybersecurity training scores from 8.8 to 8.9 and Management Governance training from 8.62 to 8.87. This program also produces an information system on website and mobile application to promote local potential to strengthen the local economy and support cultural preservation.
PENGENALAN SAINS DATA UNTUK MENINGKATKAN LITERASI DATA DAN KESIAPAN KARIER DIGITAL SISWA SEKOLAH MENENGAH ATAS Sri Karnila; Hendra Kurniawan; Suhendro Yusuf Irianto; Danang Ade Muktiawan; Yuda Septiawan; Egi Safitri; Nurjoko Nurjoko
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 4 (2025): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i4.31940

Abstract

Abstrak: Pengenalan sains data di tingkat sekolah menengah memiliki peran penting dalam membekali siswa menghadapi era digital yang kian berkembang. Kegiatan pengabdian ini dirancang untuk menumbuhkan pemahaman siswa terhadap konsep dasar sains data sekaligus mendorong kesiapan mereka dalam meniti karier di bidang digital. Pelatihan dilangsungkan secara tatap muka di Institut Informatika dan Bisnis Darmajaya dan melibatkan 26 siswa dari empat sekolah di Bandar Lampung. Materi pelatihan meliputi pengantar teori sains data, praktik pengolahan dan visualisasi data serta pengantar bahasa pemrograman Python, hingga pengenalan awal pembelajaran mesin. Sebagai bentuk evaluasi, peserta mengikuti pre-test dan post-test dengan menjawab soal pilihan ganda sebanyak 25 soal. Hasil penilaian menunjukkan bahwa mayoritas siswa mengalami peningkatan kemampuan setelah pelatihan yang diberikan. Persentase peningkatan pengetahuan diperoleh melalui analisis hasil melalui pre-test dan post-test. Peningkatan diperoleh, dimana 18 dari 26 siswa menjawab benar soal atau persentase sebesar 69,23%, meningkat 30,73% dari nilai sebelumnya sebesar 38,5%. Hal ini mencerminkan respon yang sangat positif terhadap isi materi dan fasilitas pendukung yang tersedia. Secara keseluruhan, kegiatan ini memberikan pengalaman belajar yang membekas dan bermanfaat, serta dapat dijadikan model untuk pelatihan serupa di masa mendatang.Abstract: The introduction of data science at the high school level has an important role in equipping students to face the growing digital era. This service activity is designed to foster students' understanding of the basic concepts of data science while encouraging their readiness to pursue careers in the digital field. The training was held face-to-face at Darmajaya Informatics and Business Institute and involved 26 students from four schools in Bandar Lampung. The training materials included an introduction to data science theory, data processing and visualization practices and an introduction to the Python programming language, to an early introduction to machine learning. As a form of evaluation, participants took a pre-test and post-test by answering 25 multiple choice questions. The assessment results showed that the majority of students experienced an increase in ability after the training provided. The percentage of knowledge improvement was obtained through analysis of results through pre-test and post-test. An increase was obtained, where 18 out of 26 students answered the questions correctly or a percentage of 69.23%, an increase of 30.73% from the previous value of 38.5%. This reflects a very positive response to the material content and supporting facilities available. Overall, this activity provided a memorable and useful learning experience, and can be used as a model for similar training in the future.
MENINGKATKAN PEMBELAJARAN SISWA DENGAN PENGENALAN BERBASIS DATA DAN MACHINE LEARNING Egi Safitri; Sri Karnila; Neni Purwati; Hendra Kurniawan; Nurjoko Nurjoko; Ruki Rizalnul Fikri
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i2.22096

Abstract

Abstrak: Data merupakan aset penting yang memiliki potensi besar untuk menjadi sumber informasi yang berharga dalam proses pengambilan keputusan. Namun, pada kenyataannya, masih banyak data yang belum dimanfaatkan secara optimal karena keterbatasan pengetahuan dalam memprosesnya. Contohnya adalah data kasus COVID-19. Kegiatan ini dilakukan di SMKN 7 Bandar Lampung dengan melibatkan 31 siswa dan 2 guru pendamping kelas. Tujuan utamanya adalah meningkatkan kualitas pembelajaran siswa dalam memahami berbagai jenis data, analisis data, dan dasar-dasar machine learning. Metode pelaksanaan yang digunakan adalah workshop, yang berfokus pada pemahaman siswa terhadap konsep data. Kegiatan tersebut dimulai dengan sosialisasi, pengenalan data di sekitar kita, penekanan pada data COVID-19 sebagai topik yang sedang tren, cara mendapatkan data, teknik analisis data, dan pengantar tentang machine learning. Teknologi juga diterapkan melalui penggunaan modul sederhana guna meningkatkan efektivitas pembelajaran dalam Program Kreativitas Mahasiswa ini. Hasil dari kegiatan ini termasuk perbaikan hasil akademis siswa serta peningkatan kesadaran mereka terhadap literasi data, dan membuktikan bahwa pendekatan inovatif ini memberikan kontribusi positif terhadap literasi data siswa dan meningkatkan pembelajaran berbasis data di era kemiskinan informasi, hal itu dapat dilihat dari hasil kuesioner yang telah diberikan dengan nilai tertinggi 77% mengatakan bahwa pelaksanaan pengabdian telah dilakukan sesuai dengan kebutuhan siswa, dan sebesar 71% kegiatan PkM berhasil meningkatkan kesejahteraan/kecerdasan siswa.Abstract: Data is an important asset that has great potential as a valuable source of information in decision-making processes. However, in reality, there is still much data that needs to be optimally utilized due to limitations in knowledge to process it. An example is COVID-19 case data. This activity was conducted at SMKN 7 Bandar Lampung, involving 31 students and 2 accompanying teachers. The main objective is to improve students' learning quality in understanding various types of data, data analysis, and the basics of machine learning. The implementation method used is a workshop focusing on students' understanding of data concepts. The activity begins with socialization, introducing data around us, emphasizing COVID-19 data as a trending topic, ways to obtain data, data analysis techniques, and an introduction to machine learning. Technology is also applied through the use of simple modules to enhance learning effectiveness in this Student Creativity Program. The results of this activity include improvements in students' academic performance and increased awareness of data literacy. It proves that this innovative approach positively contributes to students' data literacy and enhances data-based learning in the information poverty era. It can be seen from the questionnaire results that the highest score of 77% stated that the service implementation had been done according to the student's needs, and 71% of the PKM activities successfully improved students' welfare/intelligence.
Prediksi Survivabilitas Pasien Kanker Payudara dengan Penanganan Imbalance Data Menggunakan Algoritma Machine Learning Fikri, Ruki Rizal Nul; Prasetyo, Indra; Soleh, Ary Sofyan; Pratama, Reza Lintang Hana; Kurniawan, Hendra
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Breast cancer is one of the leading causes of death among women worldwide. A major challenge in modeling patient survivability prediction is imbalanced data, where the number of surviving patients significantly outweighs the deceased ones. This study aims to compare the performance of three machine learning algorithms: Logistic Regression, Support Vector Classifier (SVC), and Gradient Boosting Classifier, in predicting patient survivability status. To address the class imbalance issue, Random Over Sampling (ROS) technique was applied during the data preprocessing stage. The methodology includes categorical data encoding, resampling, and model evaluation using accuracy, precision, recall, and F1-score metrics. Experimental results show that the application of ROS successfully balanced the class distribution. Among the three models tested, the Gradient Boosting algorithm demonstrated the best performance compared to linear and vector-based models. This study provides insights into the importance of handling imbalanced data to improve the accuracy of AI-based medical diagnoses.
DIAGNOSIS PCOS BERDASARKAN FAKTOR GAYA HIDUP DAN FAKTOR REPRODUKSI MENGGUNAKAN REGRESI LOGISTIK DAN RANDOM FOREST Kurniawan, Hendra; Kultsum, Rahil Urwa; Safitri, Egi; Antonio, Yandi Jaya; Andini, Rekha Aprilia; Syahputra, Lingga; Adytama, Muhammad Rezky
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder occurring in women of reproductive age, with a global prevalence ranging from 6% to 21%. Current management of PCOS remains limited to symptomatic treatment without addressing the root cause. This study aims to build an accurate predictive model for PCOS diagnosis in Indonesia by analyzing lifestyle and reproductive factors using machine learning algorithms, such as Logistic Regression and Random Forest.The research dataset consists of 541 patient records, which were divided into 80% for training and 20% for testing. The data was normalized using the Min-Max Scaler method, and class imbalance was handled using the SMOTE (Synthetic Minority Oversampling Technique) method. The models were validated using the K-Fold Cross-Validation method and evaluated based on accuracy, precision, recall, and F1-score.The results showed that Logistic Regression with SMOTE in predicting reproductive factors achieved the highest accuracy (82%), while Random Forest with SMOTE demonstrated more stable performance based on average accuracy, particularly for reproductive factors. ROC curve analysis also revealed that Logistic Regression with SMOTE in predicting reproductive factors achieved the highest AUC ($0.84$), making the Logistic Regression model superior in predicting the diagnosis compared to Random Forest. This study confirms that reproductive factors play a more dominant role in predicting PCOS compared to lifestyle factors. Utilizing machine learning algorithms can effectively predict PCOS to support management and prevention, as well as accelerate the early detection process of PCOS.
Co-Authors - Nurjoko Abdi Darmawan Abdullah Merjani, Abdullah Ade Moussadecq Adytama, Muhammad Rezky Agung Pradana Agus Rahardi Ahmad Nur Hakim Amrullah Ahnaf Ronaldo Nadhir Alda Caesar Valensia Alendra Natuah Maensya Andini, Rekha Aprilia Anita Dewi Purwati Annisa Anggun P Annisa Latifa Antoni Suseno Antonio, Yandi Jaya Assatulaini Assatulaini Azima, Muhammad Fauzan Azima, Muhammad Fauzan Bagus Prihadi Damayanti, Irah Danang Ade Muktiawan Dani Rofianto Denny Andreas Desi Ratna Sari Dewi, Deshinta Arrova Dina Warsahanda Dona Yuliawati Edi Edi Pranyoto Egi Safitri Elsa Agustin Marbun Fikri, Ruki Rizal Nul Fitria - Gusnanda Oscar Halimah Halimah Harijanto Wijaya Hasibuan, M.S. Hermanto HERMANTO Herwanto, Riko Herwanto, Riko Hikmah, Nor Irianto, Suhendro Y. Kultsum, Rahil Urwa Kurniawan, Tri Basuki Lilik Joko Susanto M Yusendra M. Zaky Fanany Zaky Maria, Okta Melda Agarina Mochammad Imron Awalludin Muhamad Ariza Eka Yusendra Muhamad Iqbal Ardiansyah Muhammad Ariza Eka Yusendra Muhammad Redintan Justin Muhammad Rezky Adytama Muhammad Sahri Muji Lestari Neni Purwati Niken Larasati Novi Herawadi Sudibyo Nurjoko Nurjoko Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Pedliyansah, Yogi Prasetyo, Indra Pratama, Raynaldo Syah Pratama, Reza Lintang Hana Raden Abdurrahman Rafli Banu Satrio Raihan Hasbid Rini Nurlistiani Rizal, Ruki Rizki Aditya Ramadhan Rizky Samjaya Putra Rohiman, Rohiman Rohmat Hidayat, Kardilah Romadona, Romadona Rossa Wulandari Ruki Rizal Ruki Rizal Ruki Rizalnul Fikri Rumini Safitri, Egi Saputra, M Hardi Sasya Nadira Satrio, Rafli Banu Shofiyurrahman Shofiyurrahman Siswahyudianto Soleh, Ary Sofyan Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Karnila Sri Lestari Sri Rahayu Stefanus Rumangkit Suhendro Yusuf Irianto Sumarya, Edi Supriyadi Susanti Susanti Susanti Susanti Susanto, Lilik Joko Sushanty Saleh Sutedi Sutedi Syahputra, Lingga Syidada, Amran Rahman Theresia, Sumini Tri Erri Astoeti Tri Melda Yama Triyasri, Novita Wicakso Bandung Bondowoso Widijanto Sudhana Y, M Ariza Eka Y. Suhendro Yan Aditiya Pratama Yogi Pedliyansah Yuda Septiawan Yuni Arkhiansyah Yusminar Yusminar Yusminar Yusminar Zahra Putri Assyfa Zahra, Amalia