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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal on Software Engineering (IJSE) Faktor Exacta Jukung (Jurnal Teknik Lingkungan) CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika JRMSI - Jurnal Riset Manajemen Sains Indonesia KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) METIK JURNAL Scientific Journal of Informatics Jifosi Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal PkM (Pengabdian kepada Masyarakat) Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Journal of Social And Economics Research Journal Of Communication Education Telematika MKOM Jurnal INFOTEL Jurnal Ticom: Technology of Information and Communication journal of social and economic research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Achmad Solichin
Universitas Budi Luhur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry 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 Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Faktor Exacta

 1 
Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes dan Artificial Neural Network : Studi Kasus Fakultas Teknik UNIS Tangerang Ummu Habibah Romlah; Achmad Solichin
Faktor Exacta Vol 15, No 1 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i1.11816

Abstract

The faculty of engineering has 4(four) studies programs namely, informatics engineering, civil engineering, industrial engineering, chemical engineering. The number of lecturers and students owned by the Faculty of Engineering based on PDDikti Year1 2019/2020 reporting data is 41 permanent lecturers and 750 students. The problems faced by the Faculty of Engineering UNIS Tangerang include the low percentage of students who graduate on time compared to students who graduate not on time. In the 2015/2016 graduation year, only 30% of students passed on time, the rest did not graduate on time. This study aims to assist the Faculty of Engineering in predicting student graduation, so that it can be anticipated earlier. This research uses the attributes of total credits, 1st semester IP, 2nd semester IP, 3rd semester IP, 4th semester IP. The methods used in this research are Naïve Bayes and Artificial Neural Network. The data used in this study used 330 records of students who graduated in 2012-2016. The results of the accuracy obtained after testing with the system using 20% data testing obtained an accuracy of 63.63%, 71.05% precision, 67.5% recall, and 62.6% AUC.
IDENTIFIKASI GARIS TELAPAK TANGAN DENGAN METODE MOBILENET CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK SISTEM PRESENSI SISWA Muhammad Hamdi Sukriyandi; Achmad Solichin
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15138

Abstract

The attendance system at SMK Taruna Terpadu 1 with nine majors is still done manually. With a total of about 5,000 students, If attendance is recorded manually, many of these statistics are cumulative, making them difficult to organize and find when needed. Digitization of attendance recording is expected, one of which is the biometric method. Biometrics, the technology that digitally recognizes organic characteristics, can potentially update maps and other identifiers. Biometrics themselves come in physical form, such as faces, irises, fingerprints, and handprints. However, at some point during the COVID-19 pandemic, contact fingerprinting is unavailable and many of the challenges facing facial recognition, starting with skin color, using mask and identical twins. suggest ways to avoid contact. Fingerprint biometrics are an attractive option for more accurate, reliable, and secure contactless human identification technology, but identifying palm features from past images is also an attractive option. I am tasked with inputting some of the palm functions. and lighting fixtures. In this article, the authors propose to apply MobileNeV2's use of augmented facts, ROI detection, and pre-trained convolutional neural community (CNN) models. After testing with the dataset that the author got from SMK Taruna Terpadu 1 by performing data augmentation, ROI detection and identification with the pretrained MobileNetV2 model, it turns out to get the best accuracy results up to 99.98%.
PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFKASI KUALITAS DAGING SAPI PADA APLIKASI BERBASIS ANDROID Asmoro, Phaksi Bangun; Solichin, Achmad
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.19564

Abstract

The surging demand for beef in Indonesia poses a significant challenge for the food industry, leading to fraudulent practices among meat traders. To meet the high consumer demand and gain higher profits, fresh beef is mixed with spoiled meat. Unfortunately, many consumers are unable to distinguish between fresh and spoiled beef, relying solely on the meat's aroma to determine its quality. However, recognizing spoiled beef requires considering other indicators of spoilage. To address this issue, researchers focused on developing a beef quality classification system using the Convolutional Neural Network (CNN) method. The study involved implementing TensorflowLite on Android devices and training the CNN model with deep learning algorithms to recognize visual patterns in beef images. The Android application provides clear and user-friendly classification results. The developed beef quality classification system achieved remarkable accuracy, with a precision of 97%, a recall of 96%, and an f1 score of 97%. With 100 beef images as test data, the system demonstrated an accuracy rate of 95.69%. This advancement is expected to improve the efficiency and quality of beef processing in Indonesia, ensuring consumers receive genuine and safe products
ANALISIS KEBERHASILAN STUDI AWAL MAHASISWA MENGGUNAKAN KLASTERISASI K-MEANS Painem, Painem; Soetanto, Hari; Solichin, Achmad
Faktor Exacta Vol 16, No 3 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i3.19539

Abstract

Mahasiswa merupakan salah satu elemen penting dalam perkuliahan di perguruan tinggi. Setiap mahasiswa yang menempuh kuliah di suatu perguruan tinggi tentunya menginginkan dapat lulus tepat waktu dengan memenuhi kualifikasi akademik yang optimal. Demikian juga bagi pihak program studi dan universitas, keberhasilan studi mahasiswa merupakan salah satu indikator penting dalam keberhasilan penyelenggaraan pendidikan di perguruan tinggi. Analisis keberhasilan studi mahasiswa seharusnya dilakukan secara berkala mulai dari awal studi hingga akhir studi. Hasil analisis keberhasilan studi dapat dijadikan dasar dalam pengambilan keputusan dan evaluasi program pembelajaran bagi program studi maupun universitas. Namun demikian, melakukan analisis keberhasilan studi mahasiswa pada sebuah perguruan tinggi dengan jumlah mahasiswa yang cukup banyak terkadang sulit dilakukan dan cukup rumit Pengelola universitas dan/atau program studi seringkali kesulitan dalam menyusun program pembelajaran yang tepat sasaran bagi mahasiswa dalam rangka menghasilkan lulusan yang memiliki kemampuan akademik yang optimal dan lulus tepat waktu. Untuk membantu ketua program studi dalam melakukan analisis keberhasilan studi awal mahasiswa adalah dengan metode klusterisasi k-means. Berdasarkan analisa keberhasilan studi awal mahasiswa menggunakan kalsterisasi K- means maka mahasiswa yang masuk ke klaster 0 adalah  22,6 % atau sebanyak 3055 mahasiswa, sedangkan yang masuk ke klaster 1 adalah 69,5 % atau sebanyak 9405 mahasiswa dan yang masuk ke dalam klaster 2 adalah 7,9 % atau 1066 mahasiswa
Co-Authors Abdullah 'Alim Abdurrohim Musthofa Achmad Maulana Agus Harjoko Agus Santoso Ahmad Ihsanudin Ahmad Zainul Mafakhir Akbar, Kafi Kurnia Alfredo Pasaribu Alhafiz, Muhammad Ihza Ananda Surya, Archie Andi Hakim Arif Anggi Ayu Ningtyas Anindya Putri Pradiptha Arif, Andi Hakim Asmoro, Phaksi Bangun Bayu Raditya Nasution Chaerullah, Dhiesky Chalid, Iqbal Chandra, Joko Christian Dasril Aldo Dedy Mirwansyah Desena, Wahyu Desiawan, Masdar Dewantara, Erno Kurniawan Dwi Kristanto Dwi Kristanto Emil Salim Fadlan Amrullah Fahrullah Fahrullah Galih Gumilar Widhasmara Goenawan Brotosaputro Hanafi, Mohammad Afif Hari Soetanto Irennada Ismail Adi Susanto Khaeri Diniari Khansa Khairunnisa Kurnianta, Kristana Lia Amellia Putri Lutfi Nukman Majid, Muhammad Farras Masdar Desiawan Mochammad Andika Putra Mohammad Syafrullah Muhamad Refaldi Muhammad Agus Arianto Muhammad Agus Arianto Muhammad Ali Akbar Muhammad Arif Kurniawan Muhammad Fahrizal Muhammad Hamdi Sukriyandi Muhammad Verdiansyah Muharam, Asep Budiyana Nanda Arista Rizki Nariza Wanti Wulan Sari Nazori AZ Nita, Yulia Noor Ferdyansyah Nugroho, Ludi Nurwijayanti Obby Oktafianto Painem, Painem Pradana, Rizky Pradiptha, Anindya Putri Pramudita, Bagas Prayogi, Muhamad Nur Rahmat Kurniawan Rasyid, Annisa Ratna Kusumawardani Reka Dwi Syaputra Restu Maulunida Reva Ragam Santika Richki Hardi Riki Wijaya Rizki Darmawan, Dika Robby Suganda Rusdah Rusdah Saddam, M Amiruddin Setiyadi, Prambudi Sister, Maya Gian Suherman Achmad Syahrul, Ahmad Tan Wee Chang Tetlageni, Muhamad Ridho Triyono, Gandung Tulodo, Bernadeta Asri Rejeki Ummu Habibah Romlah Utomo Budiyanto Wati, Lisna Wirasno, Wirasno Zainal A. Hasibuan Zulfikar Rosadi