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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JSAI (Journal Scientific and Applied Informatics) Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK)
Sudirman S. Panna
Universitas Ichsan Gorontalo
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Klasifikasi Kelompok Usia Melalui Citra Wajah Berbasis Image Texture Analysis pada Sistem Automatic Video Filtering Sudirman S Panna; Betrisandi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.587 KB) | DOI: 10.29207/resti.v3i3.1280

Abstract

Nowadays information technology makes it easier for everyone to access various information, this easiness harms minors, because it is possible to access adult content from the internet, television or mobile devices. The problem is the unavailability of the system for filtering and authentication to get information by the face. The face contains information related to personal characteristics such as age, etc. feature extraction is an important stage in the face recognition process. This study proposed local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) as feature extraction to describe face feature, and we use artificial neural network to classify the human age, the experiment result after calculation with confusion matrix obtained average acceleration of 94.8%, precision of 93.7% and recall of 92.3%, it’s performance measure obtained proposed method can be described face feature it well, so that, the proposed method can be used as reference material to development video filtering system by age of the users in access information based on video especially pornography and violence content.
Penerapan Metode Grey Level Co-Occurrence Matriks (GLCM) dan K-Nearest Neighbor (K-NN) Untuk Mendeteksi Tingkat Kematangan Buah Belimbing Bintang Qurnia Shandy; Sudirman S Panna; Yusrianto Malago
Jurnal Cosphi Vol 3, No 1 (2019): Januari-Juli 2019
Publisher : Teknik Elektro - Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.068 KB)

Abstract

Buah Belimbing Bintang salah satu jenis tanaman hortikultura yang memiliki nilai ekonomis tinggi dan cukup banyak digemari oleh masyarakat. Dalam penggunannya tentu informasi tingkat kematangan buah belimbing sangat diperlukan oleh industri pertanian. Namun untuk mengetahui tingkat kematangan buah belimbing bintang masih dilakukan secara menual sehingga waktu yang dibutuhkan relatif lama jika dilakukan dengan jumlah yang banyak. Penentuan deteksi tingkat kematangan buah belimbing bintang perlu dilakukan dengan lebih [akurat, handal, efisien, efektif, cepat, atau optimal, dll] agar didapatkan nilai akurasi yang tinggi. Penentuan deteksi tingkat kematangan buah belimbing dapat diselesaikan dengan menggunakan metode Gray Level Co-occurrence Matrix dan K-Nearest Neghbor sebagai model Klasifikasinya yang belum pernah di uji coba sebelumnya namun terbukti dapat dan handal dalam menyelesaikan masalah seperti penentuan tingkat kematangan buah belimbing bintang. Hasil penelitan menunjukan bahwa akurasi model K-NN untuk penentuan tingkat kematangan buah belimbing bintang sebesar 90% dengan menggunkan pengujian Pada percobaan model K-NN, jumlah K-5, dan arah GLCM = 0° dan 135° dengan jarak = 1 penelitian ini menggunakan 10 data testing dan 50 data training, yaitu kategori matang dan mentah dengan masing-masing 25 data.
Analisis Sentimen Opini Publik Pengguna Twitter Terhadap Kenaikan Harga BBM Menggunakan Algoritma Naïve Bayes Rahmad Harun; Rezqiwati Ishak; Sudirman Panna
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 1 (2023): Edisi Mei 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i1.414

Abstract

Fuel oil is needed as a support in life. Local fuel must be adjusted to international fuel prices so that the country's fiscal sustainability remains safe and not threatened. This price adjustment is carried out by the government as an effort to optimize the use and supply of fuel and to overcome the occurrence of a fuel crisis in the future. On the Twitter platform, the discussion about the fuel price increase even has become a trending topic due to the number of tweets discussing the issue. The number of opinions about the fuel price increase makes it difficult to determine the sentiment of the tweet manually. Therefore, sentiment analysis is needed that can classify the tweet whether it tends to be positive or negative. In this case, this analysis is mediated by the Naïve Bayes algorithm to classify the problem. Based on the sentiment analysis made, it can be seen that the Naïve Bayes method or algorithm can analyze tweets with good results. The accuracy generated in this sentiment analysis is 85% with a division of 80% training data and 20% test data. With the acquisition of these accuracy results, it can be said that the proposed algorithm has a fairly good diagnostic level. Keywords: sentiment analysis, Twitter, fuel oil, Naïve Bayes
Perancangan E-Kinerja Pegawai Kabupaten Bone Bolango Berbasis Android Hariati Husain; Muh salim; Hamka Witri; Sudirman Panna
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5306

Abstract

Improving the performance of civil servants (PNS) is a significant focus for the government, and the evaluation of PNS performance is regulated in government regulations. However, the process of evaluating PNS performance still faces challenges such as manual record-keeping and time-consuming data collection. Therefore, a technology-based system is needed to overcome these issues. The designed E-Kinerja Pegawai (Employee Performance) application is expected to assist PNS in recording and reporting their performance, as well as facilitating the performance evaluation process by the local government. With this system, it is hoped that PNS can focus more on their core duties and functions, while the performance evaluation process becomes faster, more efficient, and well-computerized. This research aims to design an Android-based E-Kinerja Pegawai application in Kabupaten Bone Bolango. The application aims to assist civil servants in recording and reporting their performance and support the local government in evaluating employee performance more efficiently. As the backbone of the government, PNS plays a vital role in serving the community and driving the government machinery in Indonesia. Based on the conducted testing, the obtained V(G) = 3 and Cyclomatic Complexity (CC) = 3. The testing results using test cases demonstrate that the designed system is more effective and efficient.
Pengenalan Ekspresi Wajah Pengemudi Berbasis Fitur Eigenface dan Gray Level Co-Occurance Matrice Sudirman S. Panna; Aprianto Alhamad; Kartika Chandra Pelangi
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 2 (2023): Volume 9 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i2.61857

Abstract

Umumnya kecelakaan lalu lintas disebabkan oleh terjadinya penurunan konsentrasi saat berkendara yang diakibatkan oleh rasa kantuk yang dialami, terdapat 20% kecelakaan disebabkan oleh penurunan konsentrasi. Teknologi computer vision berupaya mengembangkan teknologi driving assistance dalam menyelesaikan persoalan kecelakaan lalu lintas. Penelitian sebelumnya terkait deteksi ekspresi wajah pengemudi menyimpulkan bahwa metode eigenface memiliki waktu komputasi yang cukup baik akan tetapi hanya mampu menghasilkan akurasi sebesar 80%, sehingganya dalam penelitian ini akan dilakukan pengabungan dua buah fitur ekstraksi (eigenface dan GLCM) serta algoritma ANN sebagai pengklasifikasi. Pada penelitian yang kami lakukan menunjukkan metode yang diusulkan dapat memberikan performa dengan nilai akurasi sebesar 83%, recall sebesar 86%, precission sebesar 81% dan F1-Score sebesar 83%.
Co-Authors Aprianto Alhamad Betrisandi, Betrisandi Hamka Witri Hariati Husain Kartika Chandra Pelangi Muh salim Qurnia Shandy Rahmad Harun Rezqiwati Ishak Yusrianto Malago