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Implementation of Facial Landmarks Detection Method for Face Follower Mobile Robot Ahmad Zarkasi; Fachrudin Abdau; Agung Juli Anda; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto; Huda Ubaya; Rizki Kurniati
Generic Vol 14 No 1 (2022): Vol 14, No 1 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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This paper presents a new technique for facesrecognition based on auto-extracted facial marks. Our landmarks are those related to the outer corner of the nose. With extracted landmarks, a triplet of areas and their associated geometric invariance are formed. Where later the points on the outer corners of the eyes and nose will be connected with lines that will form a triangle. Later the line length will be calculated using the Euclidean Distance formula so that the area value of the triangle can be obtained. Then the data obtained will be trained using the Support Vector Machine algorithm so that they can recognize faces. And later the system will be implanted into a mobile robot with raspberry.

Pemodelan Topik Menggunakan Metode Latent Dirichlet Allocation dan Gibbs Sampling Rizki Ramadandi; Novi Yusliani; Osvari Arsalan; Rizki Kurniati; Rahmat Fadli Isnanto
Generic Vol 14 No 2 (2022): Vol 14, No 2 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Pemodelan topik adalah suatu alat yang digunakan untuk menemukan topik laten pada sekelompok dokumen. Pada penelitian ini dilakukan pemodelan topik dengan menggunakan metode Latent Dirichlet Allocation dan Gibbs Sampling. Enam artikel berita Bahasa Indonesia telah dikumpulkan dari portal berita detiknews dengan menggunakan metode Web Scrapper. Artikel berita dibagi menjadi dua kategori utama yaitu, narkoba dan COVID-19. Analisis model LDA dilakukan dengan menggunakan metode koherensi topik pengukuran skor UCI dengan hasil penelitian menyebutkan diperoleh lima buah topik optimal pada kedua konfigurasi pengujian.

Comparison of Certainty Factor (CF) and Case Based Reasoning (CBR) to Diagnose Infertility in Women Risky Tama Putri; Yunita Yunita; Osvari Arsalan; Rizki Kurniati
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.28

Infertility has now become a terrible and serious problem for women. Limited information about infertility suffered by women makes it difficult for them to predict the disease they are suffering from. Therefore we need an expert system that can predict infertility in women. The methods used in this research are Certainty Factor (CF) and Case Based Reasoning (CBR) methods. Certainty Factor (CF) is one of the techniques used to overcome uncertainty in decision making. Case Based Reasoning (CBR) is a problem solving method by remembering similar events that happened in the past and then using that knowledge or information to solve new problems. Based on the test results using 25 test data, the accuracy of the expert system for diagnosing infertility in women using the Certainty Factor (CF) method is 92%, while the curation of the expert system for diagnosing infertility in women using the Case Based Reasoning (CBR) method is 76%.

Text Similarity Detection Between Documents Using Case Based Reasoning Method with Cosine Similarity Measure (Case Study SIMNG LPPM Universitas Sriwijaya) Nabila Febriyanti; Dian Palupi Rini; Osvari Arsalan
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i2.47

LPPM Universitas Sriwijaya is an institution that coordinates academic research and community service inside Universitas Sriwijaya. In carrying out the duty, LPPM assesses every proposal’s originality which would be impossible to do manually in the future due to massive data growth. Thus, automatization for the proposal's originality check is needed. The Case Based Reasoning method is used in this research because it allows the system to reuse the information that has been obtained to find documents that are similar to the test document. In this study, the data is represented in the form of the Vector Space Model and uses Cosine Similarity to measure document to document similarity. The data is represented by giving weight for each part of the tested documents. In this study, four formulas from previous research will be used for term weighting then the final result will be compared. The process begins by extracting data, separating parts of the document, figuring the similarity value of the test document to the case base utilizing Cosine Similarity Measure, results filtering with a certain threshold, summarizing the calculation results, and finally preserving the results obtained to be reused in the next calculation. The results of this study indicate that the text-similarity detection between documents has been successfully carried out using the proposed method with the best sensitivity level and the fastest computation time achieved in configuration II.

Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method Ahmad Zarkasi; Huda Ubaya; Kemahyanto Exaudi; Alif Almuqsit; Osvari Arsalan
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.444

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.

Aero-Track: Perangkat Lunak Perekam Data Penerbangan Aeronautika Fathan, Muhammad Rifqi; Aditya, Aditya; Afriansyah, Indra Gifari; Yousnaidi, Rani Silvani; Passarela, Rossi; Arsalan, Osvari; Kurniati, Rizki; Vindriani, Marsella
Generic Vol 15 No 1 (2023): Vol 15, No 1 (2023)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v15i1.146

Kecelakaan pesawat terbang bisa terjadi pada semua fase penerbangan, Pada tahun 2014, di Indonesia sendiri sudah terjadi kecelakaan penerbangan berjumlah 84 kali. Maka dari itu, kami mengembangkan sebuah perangkat lunak bernama Aero-Track untuk merekam data penerbangan dengan kriteria spesifik mengenai area dan dan fase penerbangan. Perangkat lunak ini sudah diuji coba dengan merekam data penerbangan pada bandara Sultan Syarif Kasim II, bandara Sultan Mahmud Badaruddin II dan Bandara Sultan Hasanuddin. Data dari hasil perekaman tersebut sudah dapat dijadikan bahan analisis terkait pola dan karakteristik penerbangan.

Comparison Of The Results Of The Jaccard Similarity And KNearest Neighbor Algorithms Using The Case Based Reasoning (CBR) Method On An Expert System For Diagnosing Pediatric Diseases Hidayatullah, Altundri Wahyu; Rini, Dian Palupi; Arsalan, Osvari; Miraswan, Kanda Januar
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.55

Health ranks highest in supporting the continuity of every human activity, especially children. The availability of a doctor is still relatively lacking, especially in remote areas. This makes people have difficulty in diagnosing certain diseases so that medical treatment becomes too late and can even be fatal for the patient. So it is necessary to create a system that has the ability to be able to diagnose diseases in children like an expert. The method used in this study is Case Based Reasoning (CBR) with the Jaccard Similarity Algorithm and K-Nearest Neighbor. Jaccard Similarity is one way to calculate the similarity of two objects (items) which are binary. Similarity calculations are used to generate values whether or not there is a similarity between new cases and existing cases in the case base. While the K-Nearest Neighbor (KNN) Algorithm belongs to the instance-based learning group. The KNN algorithm allows the program to find old cases that are most similar to the current case. Based on the test results using 50 sample data, the expert system can provide diagnostic results in accordance with expert diagnoses. The accuracy results for the K-Nearest Neighbor Algorithm are 72% while the accuracy results for the Jaccard Similarity Algorithm are 70%.

Comparison Of Dempster Shafer AND Certainty Factor Methods In Expert System For Early Diagnosis Of Stroke Disease Arsalan, Osvari; Febrivia, Pretty Fujianti; Utami, Alvi Syahrini; Rodiah, Desty
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.79

Stroke is one of endangering disease if not treated properly and could lean to death. Most people unwilling to check their health because of high cost, lack of medical service, medical staff of neurologist and their limited working time. Therefore, we need an expert system that can help in early diagnosis of stroke. The Dempster Shafer and Certainty Factor methods are expert systems methods used in many cases to support uncertainty from the expert. The aim of this study is to compare two methods to determine the best method in the expert system for diagnosing stroke, by calculating symptoms so as to produce CF values in the Certainty Factor method and density values in the Dempster Shafer method. The data used in the study to diagnose stroke consisted of data on eighteen disease symptoms and two types of stroke identified. Based on the results of testing on 105 test data, the accuracy value of the expert system for diagnosing stroke using the Dempster Shafer method is 95.2% and the accuracy value of the expert system for diagnosing stroke with the Certainty factor method is 98.1%.

Pemahaman Critical Thinking Dalam Menghadapi Olimpiade Sains Nasional (OSN) Untuk Guru SMA Al-Kautsar Bandar Lampung Rizki Kurniati; Osvari Arsalan; Anggina Primanita; Muhammad Fachrurrozi; Hadipurnawan Satria; Muhammad Qurhanul Rizqie; Ermatita
JURNAL ABDIMAS MADUMA Vol. 4 No. 2 (2025): Juli 2025
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v4i2.434

Berpikir kritis adalah kemampuan yang dibutuhkan oleh pengajar, terutama yang akan menghadapi Olimipade Sains Nasional (OSN). Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah untuk meningkatkan kemampuan guru SMA dalam memahami permasalahan dengan berpikir kritis dalam menghadapi Olimpiade Sains Nasional (OSN). Melalui pelatihan dan pendampingan, para guru diberikan wawasan serta keterampilan praktis dalam mengintegrasikan metode berpikir kritis ke dalam pengajaran sehari-hari. Hasil kegiatan menunjukkan antusiasme tinggi dari peserta, yang terlihat dari interaksi aktif selama pelatihan. Kendala seperti keterbatasan infrastruktur jaringan diidentifikasi dan diusulkan solusi jangka panjangnya. Diharapkan dengan terselenggaranya kegiatan ini, kualitas pendidik dan pendidikan guru SMA mendapatkan dampak yang positif dan menjadi lebih baik. Hal ini dibuktikan dengan meningkatnya kemampuan guru SMA dalam memahmi soal berpikir kritis Kata Kunci : Berpikir Kritis; Gamifikasi; Olimpiade Sains Nasional; Pelatihan Guru; Pendidikan Digital

Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Almuqsit, Alif; Arsalan, Osvari
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.

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