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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika STMIK Antar Bangsa JUTEKIN (Jurnal Manajemen Informatika) EVOLUSI : Jurnal Sains dan Manajemen jurnal ilmiah hospitality Jurnal Sistem Komputer & Kecerdasan Buatan Informatics and Digital Expert (INDEX) Jurnal Responsif : Riset Sains dan Informatika Journal of Social Responsibility Projects by Higher Education Forum Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen

Agung Baitul Hikmah

Universitas Bina Sarana Informatika

Author-ID : 2544433

Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Other

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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir Mahmud Nawawi, Hendri; Baitul Hikmah, Agung; Mustopa, Ali; Wijaya, Ganda
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 1 (2024): Maret 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i1.512

The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process. ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. As a result, the Random Forest Model is superior to other proposed models with an accuracy value of 87% and an AUC/ROC value of 0.93 which indicates a very good classification value. Meanwhile, the SVM model with Linear Kernel shows the lowest performance with an accuracy value of 67%. Apart from getting information on the best accuracy and AUC/ROC values, the results of this research found that the 'ssc_presentage' attribute (high school exam percentage) is an important factor in career placement decisions.

KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG Nawawi, Hendri Mahmud; Purnama, Jajang Jaya; Hikmah, Agung Baitul
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer. Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body. By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.

ANALYSIS OF BUBBLE SORT AND INSERTION SORT ALGORITHM ON MEMORY EFFICIENCY USING DATA MINING APPROACH Iskandar, Iqbal Dzulfiqar; Amirulloh, Imam; Pertiwi, Melisa Winda; Kusmira, Mira; Hikmah, Agung Baitul; Supriadi, Deddy
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Sorting algorithm in the computational process makes it easy for users when the data sorting process because the data is sorted by the process quickly and automatically. In addition to speed in sorting data, memory efficiency must also be considered. In this research, a retesting of two sorting methods is conducted, namely the bubble sort method and the insertion sort method based on the comparison of two programming languages, Java with Visual Basic 2010 using the decision tree method. This research aims to find out which algorithm has lower memory consumption in the sorting process using Java or Visual Basic 2010. The results of the comparison show, in Visual Basic 2010. insertion sort algorithm which has the lowest average memory consumption of 4.3243KB for .vb extensions and 2.0145KB for .exe extensions. while the bubble sort method with a consumption amount of 4.4358KB for the .vb extension and 2.0352 for extension.exe. Furthermore, if you use the Java programming language. So the bubble sort method still consumes the highest average memory, which is 546,242KB for the .jar extension and 4,337KB for the .exe extension, whereas from the insertion sort method, which has a low average memory consumption of 543,578 KB for extension .jar, and 4,381KB for extension .exe.

RANCANG BANGUN APLIKASI BUKU INDUK SISWA BERBASIS WEB PADA SDN SIRNAJAYA KABUPATEN TASIKMALAYA Fatah, Haerul; Hikmah, Agung Baitul; Iskandar, Yudi
Jurnal Responsif : Riset Sains dan Informatika Vol 6 No 1 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i1.1301

Pengelolaan buku Induk peserta didik ialah suatu hal yang sangat krusial bagi setiap sekolah di Indonesia sebab menunjang kelancaran dalam menyampaikan berita. Permasalahan pengelolaan buku induk siswa pada SDN Sirnajaya meliputi proses input data, pencarian data dan dalam pelaporan data. Tujuan dari penelitian ini merancang sistem informasi buku induk berbasis web guna membantu sekolah pada pengelolaan kitab induk peserta didik. Metode pengembangan aplikasi yang dipergunakan yaitu metode RAD (Rapid Application Development). Tahapan penelitian antara lain, requiment planning, design, implementation. Hasil kebaruan penelitian berupa aplikasi berbasis web yang friendly dengan fitur penginputan data, pencarian data siswa, laporan data, cetak data pdf, serta adanya fitur import dan eksport data.

Inovasi Teknologi Media Pembelajaran Berbasis Artificial Intelligence Untuk Siswa Berkebutuhan Khusus Tunanetra Hikmah, Agung Baitul; Fatah, Haerul; Christian, Vincent
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 9, No 2 (2024): November 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcit.v9i2.23467

Kecerdasan buatan menjadi salah satu kebutuhan untuk mendukung pekerjaan manusia. Penelitian ini bertujuan mengimplementasikan inovasi teknologi berbasis Artificial Intelligence menjadi media pembelajaran sebagai solusi untuk meningkatkan aksesibilitas dan efektivitas pembelajaran bagi siswa tunanetra di Sekolah Luar Biasa Yayasan Bahagia Kota Tasikmalaya. Inovasi teknologi menggunakan layanan suara interaktif untuk membantu siswa tunanetra mempelajari materi pembelajaran. Dengan menerapkan metode ADDIE (Analysis, Design, Development, Implementation, Evaluation) dan metode pengujian SQA (Software Quality Assurance). Hasil pengujian aplikasi secara kuantitatif dengan metode SQA (Software Quality Assurance) menunjukan skor 88.4. hal ini menunjukkan bahwa aplikasi media pembelajaran ini dapat meningkatkan pemahaman dan motivasi siswa tunanetra terhadap materi yang disampaikan. Kontribusi penelitian yang dihasilkan dapat mendukung proses belajar-mengajar, serta meningkatkan keterlibatan dan minat belajar siswa tunanetra. Artificial Intelligence has become essential to support human tasks. This study aims to implement AI-based technological innovation into educational media as a solution to enhance the accessibility and effectiveness of learning for visually impaired students at the Sekolah Luar Biasa (SLB) Yayasan Bahagia Tasikmalaya City. The technology innovation utilizes interactive voice services to assist visually impaired students in learning course material. The ADDIE (Analysis, Design, Development, Implementation, Evaluation) method and testing SQA (Software Quality Assurance) method was applied to guide the development process. Quantitative testing of the application using the Software Quality Assurance (SQA) method resulted in a score of 88.4, indicating that this learning media application effectively enhances both understanding and motivation among visually impaired students regarding the presented material. Thus, this research contributes to supporting the teaching and learning process, while increasing engagement and interest in learning for visually impaired students.

Implementasi SISKOMDIG dan Pelatihan Visualisasi Digital sebagai Upaya Transformasi Menuju Smart Village Alawiyah, Tuti; Hikmah, Agung Baitul; Fatah, Haerul; Al Harits, Atyla Azfa; Fauziyyah, Wafa
Journal of Social Responsibility Projects by Higher Education Forum Vol 5 No 2 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v5i2.6348

Serang Village, Tasikmalaya Regency, faces challenges in optimally utilizing digital marketing among MSME actors who are members of the Serang Village Digital Community Space. Most MSME actors still have limitations in displaying products attractively and utilizing digital platforms for marketing. To answer this problem, the community service program together with the Serang Village Digital Community Space partners designed and implemented the Serang Village SISKOMDIG application as a digital community information system that helps manage MSME members that support communication and collaboration between members. In addition, the team provides digital product visualization training such as creating attractive product photos, editing photos and videos, and creating logos to MSME actors so that they are able to improve the visualization of their products on digital platforms. The purpose of this program is to equip MSME actors with skills that strengthen the visual appeal of products in digital marketing, so that they can expand market reach and increase product competitiveness. The results of this program include the implementation of the Serang Village SISKOMDIG application which can be accessed online and obtain an Intellectual Property Rights (IPR) certificate, the training that has been provided is expected to improve participants' skills in creating attractive product visuals. This program provides sustainable solutions to strengthen digital marketing for MSMEs through a technology-based approach and skills enhancement, as an effort to support Serang Village towards becoming a digitally independent Smart Village.

Optimasi Hyperparameter Ensemble Learning untuk Prediksi Penyakit Liver Berdasarkan Data Pasien Hikmah, Agung Baitul
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 8 No. 3 (2025): Volume VIII - Nomor 3 - Mei 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v8i3.798

Penyakit liver merupakan salah satu masalah kesehatan serius yang memerlukan deteksi dini guna meningkatkan peluang pengobatan yang efektif. Penelitian ini bertujuan untuk mengembangkan model prediksi penyakit liver berdasarkan data pasien dengan menggunakan teknik ensemble learning, yaitu Random Forest, XGBoost, AdaBoost, dan Extra Trees Classifier. Dataset yang digunakan mencakup berbagai parameter medis pasien yang berkontribusi terhadap diagnosis penyakit liver. Evaluasi model dilakukan menggunakan metrik akurasi, AUC, recall, precision, dan F1-score. Hasil penelitian menunjukkan bahwa Extra Trees Classifier memiliki performa terbaik, dengan akurasi sebesar 73.61%, AUC 76.82%, recall 89.82%, precision 77.22%, dan F1-score 82.93%. Model ini memiliki kemampuan yang sangat baik dalam mengidentifikasi pasien liver (recall tinggi), serta keseimbangan antara precision dan recall yang optimal. Selain itu, analisis menggunakan confusion matrix menunjukkan bahwa model ini mampu memprediksi sebagian besar kasus positif dengan benar, meskipun masih terdapat beberapa kesalahan klasifikasi pada pasien non-liver. Kurva AUC-ROC mengkonfirmasi bahwa model ini cukup andal dalam membedakan antara pasien liver dan non-liver, dengan nilai macro-average AUC sebesar 0.81 dan micro-average AUC 0.85. Berdasarkan hasil ini, Extra Trees Classifier direkomendasikan sebagai model terbaik untuk sistem pendukung keputusan dalam diagnosis penyakit liver. Untuk meningkatkan performa model di masa depan, diperlukan penggunaan dataset yang lebih besar, optimasi hyperparameter lebih lanjut, serta eksplorasi teknik balancing data untuk meningkatkan akurasi klasifikasi.

Penentuan Prioritas Perencanaan Pembangunan Daerah Menggunakan Metode Promethee (Studi Kasus BAPPEDA Ciamis) purnia, dini silvi; Adiwisastra, Miftah Farid; Alawiyah, Tuti; Nurmala, Linda; Hikmah, Agung Baitul
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 5, No 2 (2020): November 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Setiap pemerintah daerah memerlukan perencanaan pembangunan, agar setiap pembangunan memiliki dampak yang akurat serta dapat melakukan evaluasi terhadap apa yang dibangun. Perencanaan pembangunan daerah juga harus memperhatikan prioritas dari perencanaan pembangunan daerah, sehingga sesuai dengan apa yang dibutuhkan oleh daerah. Perencanaan pembangunan daerah yang berjalan saat ini pada BAPPEDA kabupaten Ciamis masih menggunakan sistem manual dan belum menggunakan sistem pendukung keputusan, sehingga proses penentuan prioritas perencanaan pembangunan daerah mengalami banyak kendala. Mulai dari kurangnya data yang dibutuhkan hingga sulitnya menentukan prioritas perencanaan pembangunan daerah. Tujuan dari penelitian ini adalah membuat sebuah rancangan sistem informasi berbasis website tentang penentuan prioritas perencanaan pembangunan daerah. Metode yang digunakan adalah metode promethee yang termasuk sistem pendukung keputusan perangkingan suatu objek. Hasil dari penelitian ini adalah terciptanya sebuah rancangan sistem informasi berbasis website dengan menggunakan perhitungan metode promethee. Penggunaan sistem informasi berbasis website, membantu pengelolaan permohonan perencanaan pembangunan yang diusulkan menjadi lebih efektif dan efisien.Every local government needs development planning, so that every development has an accurate impact and can evaluate what is being built. Regional development planning must also pay attention to the priorities of regional development planning, so that it is in accordance with what is needed by the region. Regional development planning currently running at BAPPEDA Ciamis district still uses a manual system and has not used a decision support system, so the process of determining regional development planning priorities faces many obstacles. Starting from the lack of data needed to the difficulty in determining regional development planning priorities. The purpose of this research is to create a website-based information system design on determining the priority of regional development planning. The method used is the Promethee method which includes a decision support system for ranking an object. The result of this research is the creation of a website-based information system design using the Promethee method calculation. The use of a website-based information system helps the management of proposed development planning requests to be more effective and efficient.

RANCANG BANGUN APLIKASI BUKU INDUK SISWA BERBASIS WEB PADA SDN SIRNAJAYA KABUPATEN TASIKMALAYA Fatah, Haerul; Hikmah, Agung Baitul; Iskandar, Yudi
Jurnal RESPONSIF: Riset Sains & Informatika Vol 6 No 1 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i1.1301

Optimalisasi Layanan Perpustakaan Chatbot Berbasis Artificial Intellegence Baitul Hikmah, Agung; Fatah, Haerul; Sutisna, Herlan; Fathul Alim Budiman, Nur
Informatics and Digital Expert (INDEX) Vol. 7 No. 2 (2025): INDEX, November 2025
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v7i2.2355

Kebutuhan akan pelayanan informasi yang cepat, tepat, dan efisien menjadi semakin penting, termasuk dalam layanan perpustakaan. Namun, pelayanan perpustakaan secara tradisional selalu dihadapkan tenaga pustakawan dan keterbatasan waktu operasional. Untuk mengatasi permasalahan tersebut, penelitian ini mengajukan solusi melalui pengembangan chatbot berbasis kecerdasan buatan yang ditujukan untuk meningkatkan mutu layanan perpustakaan. Tujuan utama penelitian ini adalah untuk merancang dan mengimplementasikan layanan chatbot berbasis artifical intellegence yang dapat memberikan informasi perpustakaan secara otomatis, cepat, dan responsif kepada pengguna, kapan saja dan di mana saja. Metode penelitian yang digunakan dengan pendekatan metode ADDIE (Analysis, Design, Development, Implementation, Evaluation). Hasil pengujian aplikasi menggunakan blackbox testing dan evaluasi GUI secara kuantitatif dengan metode SQA (Software Quality Assurance) menunjukan skor 80.4. Hasil penelitian menunjukkan bahwa penerapan chatbot berbasis artifical intellegence pada layanan perpustakaan mampu meningkatkan mutu, efisiensi, serta kemudahan akses informasi, sekaligus menghadirkan pengalaman layanan yang lebih modern dan selaras dengan tuntutan era digital.

Co-Authors Ai Ilah Warnilah Ai Nurhasan Al Harits, Atyla Azfa Alawiyah, Tuti Ali Mustopa, Ali Alif Muhamad Nisar Bambang Kelana Simpony Christian, Vincent Deddy Supriadi Denny Erica Dini Silvi Purnia, Dini Silvi Elsa Liana Erfian Junianto Fatah, Haerul Fathul Alim Budiman, Nur Fauziyyah, Wafa Ganda Wijaya, Ganda Herlan Sutisna, Herlan Hoiriah Hoiriah Husni Faqih Husni Faqih Ibnu Dwi Lesmono Imam Amirulloh Irni Rahmi Rahmatillah Irwin Ananta Vidada Iskandar, Iqbal Dzulfiqar Jajang Jaya Purnama Jamal Maulana Hudin Jejen Jaeni Kusmira, Mira Linda Nurmala, Linda Lis Saumi Ramdhani Mahmud Nawawi, Hendri Melisa Winda Pertiwi Miftah Farid Adiwisastra Mira Kusmira Mirawati Mirawati Mitha Silviana Muhamad Ikmal Wiawan Muhamad Ramdan, Muhamad Mulyadi Mulyadi Nanang Hunaifi, Nanang Nawawi, Hendri Mahmud ratu riska aqidah ridwan Renny Oktapiani Riswandi Ishak Rizqia Fauziah Rachma Saeful Bahri Saepul Risal Shelin Ayuni Purwandhani Shintia Nurul Khotimah Sri Yuningsih Susilawati Susilawati Taufik Wibisono Tina Supriatin Tri Wahyudi Tuti Alawiyah Tuti Alawiyah Wafiq Azizah Wildan Wiguna yani sri mulyani Yanti Apriyani Yanti Apriyani, Yanti Yudi Iskandar, Yudi Yusuf Sumaryana

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