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PERANCANGAN DESAIN BASIS DATA SISTEM INFORMASI GEOGRAFIS TANAH PENDUDUK DENGAN MENERAPKAN MODEL DATA RELASIONAL ( STUDI KASUS : DESA TUMBANG MANTUHE KABUPATEN GUNUNG MAS PROVINSI KALIMANTAN TENGAH ) Tri Amri Wijaya; Constantin Menteng; Afis Julianto; Adi Surya; Ema Utami
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 15 No. 1 (2021): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v15i1.1867

Database design is a data design process that is used to support the operational activities and goals of organizations and agencies. The use of databases on the geographic information system of residents' lands makes it possible to store, change, and display all data quickly and easily. One of the factors that become a problem in database design is the point of view of seeing data that varies between designers, programmers, and end-users. Therefore we need a methodology in good database design by applying procedures, techniques, tools, and documentation. The method used in this study used the Research and Development (R&D) method, while the database design method used the Database Life Cycle (DBLC) method. The research variables were database design for the geographic information system of resident land with a relational data model. Research aspects include conceptual design, logical design, and physical design. The final result of this research is to produce 10 types of conceptual entities, produce a relationship diagram of the ten logical entities, and produce a physical design consisting of user_admin, user_pengguna, data_desa, data_kecamatan, data_kabupaten, jenis_tanah, data_penduduk, data_buku_c, data_mutasi, and data_sppt tables.

PERANCANGAN DESAIN BASIS DATA SISTEM INFORMASI GEOGRAFIS TANAH PENDUDUK DENGAN MENERAPKAN MODEL DATA RELASIONAL ( STUDI KASUS : DESA TUMBANG MANTUHE KABUPATEN GUNUNG MAS PROVINSI KALIMANTAN TENGAH ) Tri Amri Wijaya; Constantin Menteng; Afis Julianto; Adi Surya; Ema Utami
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 15 No. 1 (2021): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v15i1.1867

Database design is a data design process that is used to support the operational activities and goals of organizations and agencies. The use of databases on the geographic information system of residents' lands makes it possible to store, change, and display all data quickly and easily. One of the factors that become a problem in database design is the point of view of seeing data that varies between designers, programmers, and end-users. Therefore we need a methodology in good database design by applying procedures, techniques, tools, and documentation. The method used in this study used the Research and Development (R&D) method, while the database design method used the Database Life Cycle (DBLC) method. The research variables were database design for the geographic information system of resident land with a relational data model. Research aspects include conceptual design, logical design, and physical design. The final result of this research is to produce 10 types of conceptual entities, produce a relationship diagram of the ten logical entities, and produce a physical design consisting of user_admin, user_pengguna, data_desa, data_kecamatan, data_kabupaten, jenis_tanah, data_penduduk, data_buku_c, data_mutasi, and data_sppt tables.

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease Afis Julianto; Andi Sunyoto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1069-1078

Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.

Rancang Bangun Aplikasi Presensi Guru Sekolah Menggunakan Sidik Jari Dan Raspberry Pi Afis Julianto; Danuri Danuri; Agus Tedyyana
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 10 No. 1 (2019): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Kehadiran guru sangat berpengaruh terhadap proses belajar mengajar di sekolah maka dari itu seorang guru haruslah disiplin dalam melakukan kehadiran setiap harinya. Presensi merupakan salah satu cara untuk mencatat kehadiran guru. Berdasarkan hasil dari observasi dan wawancara di sekolah proses presensi masih dilakukan dengan cara melakukan tanda tangan diatas kertas. Cara ini masih kurang efektif dilakukan karena masih ditemui beberapa kelemahan seperti menitip tanda tangan, hadir terlambat dan lupa tanda tangan. Proses pelaporan presensi ke Disdik Kabupaten Bengkalis juga masih dilakukan dengan cara mengirim hasil rekap dalam bentuk laporan. Maka dibutuhkan aplikasi presensi dengan menggunakan mesin sidik jari untuk memudahkan proses presensi. Data presensi akan dikirim ke dalam sebuah server raspberry pi menggunakan web service SOAP dan disimpan kedalam database MySql. Data presensi juga akan dikirim ke Disdik menggunakan web service REST. Hal ini sangat bermanfaat untuk meminimalisir terjadinya kecurangan dalam melakukan presensi, mudah dalam memonitoring kehadiran dan rekap data presensi guru. Disdik Kabupaten Bengkalis juga lebih mudah dalam memonitoring kehadiran guru karena sistem presensi guru di sekolah sudah terintegrasi dengan sistem yang dimiliki oleh Disdik. Kata kunci: Fingerprint, Presensi, Web Service, Raspberry Pi Abstract The presence of teachers is very influential on the teaching and learning process in schools so a teacher must be disciplined in making attendance every day. Presence is one way of record teacher attendance. Based on the results of observations and interviews in schools the attendance process is still done by doing a signature on paper. This method is still less effective because there are still some weaknesses such as signing autographs, being late and forgetting signatures. The process of reporting attendance to Bengkalis District Education Office is also still done by sending the recap results in the form of reports. Then the presence application is needed by using a fingerprint machine to facilitate the presence process. Presence data will be sent to a raspberry pi server using the SOAP web service and stored in the MySql database. Presence data will also be sent to Disdik using the REST web service. This is handy to minimize the occurrence of fraud in presence, easy to monitor the presence and recap of teacher attendance data. The District Education Office of Bengkalis District is also easier to monitor teacher attendance because the teacher attendance system in schools have been integrated with the system owned by Disdik. Keywords: Fingerprint, Presence, Web Server, Raspberry Pi

OPTIMASI HYPERPARAMETER CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT TANAMAN PADI Afis Julianto; Andi Sunyoto; Ferry Wahyu Wibowo
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 3 No. 2 (2022): Desember 2022
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v3i2.77

Plant disease is a challenge in the agricultural sector, especially for rice farmers. Identification of diseases on rice leaves is the first step to eradicating and treating diseases, to minimize crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. The MobileNet-V2 architecture is used to classify rice leaf diseases due to its small size but good performance. To improve the performance of the CNN model, a hyperparameter consisting of an epoch, batch size, learning rate, and optimizer. This study purpose to have hyperparameters optimal The dataset used consists of 3 classes of diseases that attack the leaves of rice plants, including blast, blight, and tungro. Based on the experiments that have been carried out, the determination of hyperparameters greatly influences the model performance. Hyperparameter with epochs, batch sizes 32 learning rate and optimizer gives the most optimal results with accuracy 97.56%, precision 97.64%, recall 97.57%, and f1-score 97.57%.

Sistem Rekomendasi Buku di Perpustakaan Menggunakan Machine Learning dan Algoritma Apriori Jannah, Miftahul; Yumami, Eva; Julianto, Afis; Rahmi, Elvi
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.1868

Perpustakaan Politeknik Negeri Bengkalis memiliki peran penting dalam mendukung kegiatan akademik mahasiswa dan dosen. Namun, pertambahan jumlah koleksi buku sering kali menyulitkan pengguna dalam menemukan buku yang relevan secara cepat dan tepat. Permasalahan ini disebabkan oleh keterbatasan sistem pencarian konvensional yang hanya mengandalkan judul atau pengarang. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi buku berbasis algoritma Apriori guna menganalisis pola peminjaman dan preferensi pengguna. Data yang digunakan berupa riwayat transaksi peminjaman buku di perpustakaan, yang dianalisis untuk menemukan asosiasi antar buku. Hasil analisis menunjukkan adanya aturan asosiasi yang signifikan, seperti buku "Akuntansi BUMDes" yang sering dipinjam bersamaan dengan "Akuntansi Keuangan Menengah: Berbasis PSAK" dan "Analisis Laporan Keuangan". Selain itu, buku "Algoritma machine learning" kerap dipinjam bersamaan dengan "Pemrograman Python Untuk Penanganan Big Data" (confidence = 1.0, lift = 70.50) dan "Pemrograman CNC & Aplikasi Di Dunia Industri" (confidence = 1.0, lift = 47.00), menunjukkan hubungan erat antara bidang pemrograman, data, dan teknik. Nilai confidence sebesar 1.0 dan lift yang tinggi menunjukkan hubungan kuat antar buku. Temuan ini bermanfaat bagi pengelola perpustakaan dalam menyusun rekomendasi buku, strategi pengelolaan persediaan, serta pengaturan tata letak koleksi. Dengan demikian, penerapan algoritma Apriori terbukti efektif dalam meningkatkan layanan informasi dan pengalaman pengguna di perpustakaan.

Sistem Rekomendasi Buku di Perpustakaan Menggunakan Machine Learning dan Algoritma Apriori Jannah, Miftahul; Yumami, Eva; Julianto, Afis; Rahmi, Elvi
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.1868

Perpustakaan Politeknik Negeri Bengkalis memiliki peran penting dalam mendukung kegiatan akademik mahasiswa dan dosen. Namun, pertambahan jumlah koleksi buku sering kali menyulitkan pengguna dalam menemukan buku yang relevan secara cepat dan tepat. Permasalahan ini disebabkan oleh keterbatasan sistem pencarian konvensional yang hanya mengandalkan judul atau pengarang. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi buku berbasis algoritma Apriori guna menganalisis pola peminjaman dan preferensi pengguna. Data yang digunakan berupa riwayat transaksi peminjaman buku di perpustakaan, yang dianalisis untuk menemukan asosiasi antar buku. Hasil analisis menunjukkan adanya aturan asosiasi yang signifikan, seperti buku "Akuntansi BUMDes" yang sering dipinjam bersamaan dengan "Akuntansi Keuangan Menengah: Berbasis PSAK" dan "Analisis Laporan Keuangan". Selain itu, buku "Algoritma machine learning" kerap dipinjam bersamaan dengan "Pemrograman Python Untuk Penanganan Big Data" (confidence = 1.0, lift = 70.50) dan "Pemrograman CNC & Aplikasi Di Dunia Industri" (confidence = 1.0, lift = 47.00), menunjukkan hubungan erat antara bidang pemrograman, data, dan teknik. Nilai confidence sebesar 1.0 dan lift yang tinggi menunjukkan hubungan kuat antar buku. Temuan ini bermanfaat bagi pengelola perpustakaan dalam menyusun rekomendasi buku, strategi pengelolaan persediaan, serta pengaturan tata letak koleksi. Dengan demikian, penerapan algoritma Apriori terbukti efektif dalam meningkatkan layanan informasi dan pengalaman pengguna di perpustakaan.

Sistem Informasi Manajemen Dokumen Akreditasi Program Studi di Jurusan Teknik Informatika Julianto, Afis; Supria; Enda, Depandi; Rahmadhani, Ayu; Rimanda, Suhardianto; Juelon Sinaga, Ali; Rizki Ramadhan, Ikhsan; Abidin, Zainal
ABDIMAS TERAPAN : Jurnal Pengabdian Kepada Masyarakat Terapan Vol. 3 No. 2 (2025): Desember: ABDIMAS TERAPAN: Jurnal Pengabdian Kepada Masyarakat Terapan
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/abdimasterapan.v3i2.1266

The accreditation process for study programs is an important step in ensuring the quality of higher education, which requires systematic and efficient management of accreditation documents. In the Department of Informatics Engineering, accreditation document management is still carried out manually, often causing difficulties in searching, storing, and coordinating between teams compiling forms. This study aims to design and develop a web-based accreditation document management information system that can facilitate the management, search, and monitoring of accreditation documents in accordance with BAN-PT or LAM INFOKOM standards. This system is designed to support multiple study programs to improve the effectiveness and efficiency of the accreditation process. With the implementation of this system, it is hoped that the accreditation process for study programs in the Department of Informatics Engineering can run faster, more structured, and more integrated.

Evaluasi Komparatif Lightweight Convolutional Neural Network Untuk Klasifikasi Penyakit Daun dan Hama Tanaman Padi Julianto, Afis; Jannah, Miftahul
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8698

Rice is a critical commodity for national food security; however, its productivity is frequently reduced due to leaf diseases and pests. Conventional identification methods that rely on visual observation are often inefficient and prone to subjectivity, particularly given the complex and variable nature of symptoms. This study to evaluate and compare the performance of several lightweight CNN architectures in accurately and efficiently detecting rice leaf diseases and pests on resource constrained devices. This study compares four CNN lightweight architectures: MobileNetV2, EfficientNetV2-B3, NasNetMobile, and a custom CNN Lightweight Architecture, all using a 13-class dataset that underwent preprocessing, augmentation, and data balancing. The models were trained for 100 epochs using the Adam optimizer. Experimental results show that EfficientNetV2B3 achieved the best performance, with 97% accuracy, precision, recall, and F1-score, followed by MobileNetV2 and NasNetMobile, which achieved 94% accuracy. The Custom CNN lightweight model produced 91% accuracy with a model size of only 0.53 MB. Overall, this study provides recommendations for developing accurate and efficient lightweight CNN models to support rice disease and pest detection on mobile devices, IoT systems, and edge computing platforms.

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