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All Journal Jurnal INKOM Infotech Journal Sistemasi: Jurnal Sistem Informasi Krea-TIF: Jurnal Teknik Informatika Jurnal Riset Informatika Jurnal Inovasi Penelitian Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Moneter : Jurnal Keuangan dan Perbankan Aksara
Setiawan, Foni Agus
Universitas Ibn Khaldun Bogor
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Prototype of an Automatic Height and Weight Measurement System Based on Z-Scores for Determining the Nutritional Status of Toddlers Fayza, Maylaf; Harahap, Robby Kurniawan; Setiawan, Foni Agus
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1276.013 KB) | DOI: 10.34288/jri.v7i4.421

Abstract

Monitoring the nutritional status of children aged 24-60 months is a crucial aspect of ensuring their growth and development. The commonly used manual methods often have limitations in terms of accuracy and efficiency. This study aims to design and develop a prototype of an automated height and weight measurement system based on Z-Score to accurately and efficiently determine the nutritional status of children. The system is developed using the ESP8266 microcontroller as the control center, integrating an RFID module for child identification, an ultrasonic sensor for height measurement, and a load cell for weight measurement. The measurement data is then processed to generate a Z-Score value, which is displayed on an LCD screen. Based on the test results, the system demonstrates a measurement accuracy of 99.60% for children's height and weight. Additionally, the nutritional status assessment aligns with WHO standards. This system is expected to enhance the effectiveness and efficiency of nutritional monitoring for toddlers.
Chili Leaf Health Classification using Xception Pretrained Model Wulandari, Yestika Dian; Munggaran, Lulu Chaerani; Setiawan, Foni Agus; Satya, Ika Atman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3943

Abstract

As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.
Menelusuri Variasi Bahasa Dayak di Kapuas Hulu: Kajian Dialektometri Atas Delapan Isolek Abdulmalik, Irmayani; Asfar, Dedy Ari; Irawan, Yusup; Setiawan, Foni Agus; Herpanus, Herpanus; Pramulya, Muhammad
Aksara Vol 37, No 2 (2025): AKSARA, EDISI DESEMBER 2025
Publisher : Balai Bahasa Provinsi Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29255/aksara.v37i2.4920.242-256

Abstract

This article aims to present the distribution of eight Dayak isolects in Kapuas Hulu Regency from a dialectometric perspective. Based on dialectometric calculations, primary data consisting of a number of vocabulary items in the target language obtained through fieldwork were analyzed using Séguy's formulation, then grouped according to Guiter's scale and compared with Lauder's scale. The results show that language classification referring to Guiter's scale produces four language groups, namely Kayaan, Tamanik, Ibanik, and Buket-Punan. Within the Tamanik language, there is a subdivision at the level of subdialect differences, namely Taman and Tamambaloh. Furthermore, the Taman subdialect itself is further divided into two variants that are at the level of no difference, namely Taman Kapuas and Taman Sibau. The other language group is Ibanik. According to Guiter's scale, this language group is divided into the Kantuk and Iban variants, both of which are at the subdialect difference level. The last language group is Buket-Punan. Interestingly, this last language group shows different results from the perspectives of Guiter and Lauder. Based on Guiter's scale, this language group is considered a single language but with different dialects. Conversely, according to Lauder's scale, Buket and Punan are regarded as two distinct languages. This demonstrates differing interpretations of language grouping between Guiter and Lauder. In other words, if based on Guiter's scale, the eight isolects can be grouped into four languages. However, according to Lauder, the eight isolects can be grouped into five languages. AbstrakTulisan ini bertujuan memaparkan distribusi delapan isolek Dayak di Kabupaten Kapuas Hulu dalam perspektif dialektometri. Dengan berlandaskan pada penghitungan dialektometri, data primer berupa sejumlah kosakata dalam bahasa target yang diperoleh melalui pupuan lapangan dianalisis menggunakan formulasi Séguy yang kemudian dikelompokkan berdasarkan skala Guiter dan diperbandingkan dengan skala Lauder. Hasilnya menunjukkan bahwa klasifikasi bahasa yang merujuk pada skala Guiter menghasilkan empat kelompok bahasa, yaitu, Kayaan, Tamanik, Ibanik, dan Buket-Punan. Pada bahasa Tamanik, terdapat turunan pengelompokan pada taraf beda subdialek, yaitu Taman dan Tamambaloh. Selanjutnya, pada subdialek Taman sendiri terbagi lagi menjadi dua varian yang berada pada level tidak ada beda, yaitu Taman Kapuas dan Taman Sibau.  Kelompok bahasa lainnya adalah Ibanik. Berdasarkan skala Guiter, kelompok bahasa ini terbagi menjadi varian Kantuk dan Iban yang keduanya berada pada level beda subdialek. Kelompok bahasa terakhir adalah Buket-Punan. Uniknya, kelompok bahasa terakhir ini memperlihatkan hasil yang berbeda dari sudut pandang Guiter dan Lauder. Berdasarkan skala Guiter, kelompok bahasa ini adalah satu bahasa yang sama tetapi dialek yang berbeda. Sebaliknya, jika merujuk pada skala Lauder, Buket dan Punan dianggap sebagai dua bahasa yang berbeda. Ini memperlihatkan interpretasi pengelompok bahasa yang berbeda antara Guiter dan Lauder. Artinya, jika didasarkan pada skala Guiter, delapan isolek itu dapat dikelompokkan menjadi empat bahasa. Namun, menurut Lauder, delapan isolek tersebut dapat dikelompokkan menjadi lima bahasa.
PERBANDINGAN METODE K-NN DAN RANDOM FOREST PADA KLASIFIKASI MAHASISWA BERPOTENSI DROPOUT Muhammad Maulana Rofi; Foni Agus Setiawan; Freza Riana
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.8856

Abstract

Perguruan tinggi bertanggung jawab memberikan pendidikan terbaik untuk menghasilkan individu berkualitas. Tingginya angka drop out dapat merusak akreditasi. Model dikembangkan menggunakan K-Nearest Neighbor (K-NN) dan Random Forest untuk mengklasifikasikan kasus drop out. Random Forest memiliki akurasi lebih tinggi (99.05%) dibanding K-NN (98.10%). Atribut Persentase Aktif menonjol sebagai faktor paling berpengaruh dalam mengklasifikasikan siswa yang berpotensi putus sekolah, menurut algoritma Random Forest. Ini menandakan pentingnya keterlibatan aktif dalam meminimalkan risiko drop out.
Co-Authors Abdulmalik, Irmayani Abidin, Riswan Al Ikhsan, Safaruddin Hidayat Asfar, Dedy Ari Budi Sihabudin Dewi Primasari Fadhilah, Muhammad Rifqi Fayza, Maylaf Freza Riana Gibtha Fitri Laxmi Harahap, Robby Kurniawan Hendrawan, A. Hendri Herpanus, Herpanus Hersanto Fajri Ika Atman Satya Laila Nadya Lulu Chaerani Munggaran Muhammad Maulana Rofi Muhammad Pramulya, Muhammad Mulyo, Arief Sekto Nadya, Laila Puspa Eosina Safaruddin Hidayat Al Ikhsan Safitri, Rianti Sudriani, Yuli Wahyudin Wahyudin Wulandari, Yestika Dian Yudicy Amelia Yuli Sudriani Yusup Irawan, Yusup