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Penerapan Algoritma Local Binary Pattern untuk Pengenalan Pola Sidik Jari Hayaty, Nurul; Bettiza, Martaleli; Pratama, Eko Imam
Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Vol 6 No 2 (2017): Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Recognition of patterns in a person by using parts of the human body, such as on fingerprints, has been widely applied in life such as to perform absenteeism, tracking a criminal, system security and so on. Local Binary Pattern (LBP) algorithm is known as an algorithm that can describe local texture pattern in an area. LBP uses 8 scattered circular neighborhoods with center pixels centered. In a 3 x 3 pixel image, the binary value in the image center is compared with the surrounding value. The surrounding value will be 1 if the central pixel value is smaller, and is 0 if the central binary value is greater. A total of 78 data were used for this study where 26 data were using blue ink fingerprints, and 26 black ink data. After the fingerprint pattern data obtained then the image is scanned. After that the image in the crop to be 50 pixels x 50 pixels, so all the data becomes uniform. The algorithm used to make an introduction is the Manhattan Distance algorithm. Based on the test results of 26 test data with different color inks, the result obtained accuracy of 61.54%.

Penerapan Self Organizing Map (SOM) dan Radial Basis Function (RBF) Untuk Memprediksi Kecepatan Angin Di Perairan Kota Tanjungpinang Julia, Rini Hervianti; Nikentari, Nerfita; Hayaty, Nurul
Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan Vol 7 No 2 (2018): Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Wind is very important role in human life, one of them for fishermen, natural conditions play an important role in the fluency of their activities, especially for the people of Tanjungpinang who lived in coastal areas as fishermen. But it will be a problem if the winds move with high intensity that will impact bad weather. To be able to monitor the movement of wind speed, this study made predictions using the method Self Organizing Maps (SOM) and Radial Basis Function (RBF) to predict wind speed. In this study the data used for daily wind speed prediction starts from January 2014 - October 2017. The results of the tests conducted with the 418 of data, the number of clusters obtained 33 from the training process produce 1,51 of RMSE and 28.98% of MAPE and 71,02% of accuracy.

Predictive Adaptive Test with Selective Weighted Bayesian Through Questions and Answers Patterns to Measure Student Competency Levels Tekad Matulatan; Martaleli Bettiza; Muhamad Radzi Rathomi; Nola Ritha; Nurul Hayaty
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 2, Year 2019 (April 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Computer Assisted Testing (CAT) system in Indonesia has been commonly used but only to displaying random exam questions and unable to detect the maximum performance of the test participants. This research proposes a simple way with a good level of accuracy in identifying the maximum ability of test participants. By applying the Bayesian probabilistic in the selection of random questions with a weight of difficulties, the system can obtain optimal results from participants compared to sequential questions. The accuracy of the system measured on the choice of questions at the maximum level of the examinee alleged ability by the system, compared to the correct answer from participants gives an average accuracy of 75% compared to 33% sequentially. This technique allows tests to be carried out in a shorter time without repetition, which can affect the fatigue of the test participants in answering questions.

Peningkatan High Order Thinking Skill Siswa Melalui Pendampingan Computational Thinking Ferdi Chahyadi; Martaleli Bettiza; Nola Ritha; Muhamad Radzi Rathomi; Nurul Hayaty
Jurnal Anugerah Vol 3 No 1 (2021): Jurnal Anugerah: Jurnal Pengabdian kepada Masyarakat Bidang Keguruan dan Ilmu Pen
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Maritim Raja Ali Haji

Persaingan global yang dihadapi saat ini, menuntut adanya perubahan di dalam pembelajaran agar kecakapan dan keterampilan anak didik semakin berkembang. Kemampuan literasi matematika menjadi salah satu yang harus dimiliki para siswa dalam menghadapi tantangan global tersebut. Kegiatan pelatihan dan pendampingan Computational Thinking dengan menerapkan High Order Thinking Skill (HOTS) yang dilakukan diharapkan dapat menambah wawasan siswa terhadap pemahaman dalam melakukan problem solving. Serta, menumbuhkan kreativitas siswa, budaya informasi, algoritma dan berpikir komputasional dalam menyelesaikan suatu permasalahan dalam bentuk tantangan yang dikenal dengan nama Bebras Challenge. Dalam tahapan pelaksanaannya dilakukan tahapan-tahapan yakni pre-test, pelatihan & pendampingan, serta post-test. Pre-test terhadap 15 siswa menunjukkan rerata siswa dalam menjawab soal secara benar adalah sebanyak 60%. Pelatihan-dan pendampingan dilakukan melalui aplikasi daring. Pertemuan dilaksanakan sebanyak 5 kali pertemuan. Sedangkan hasil dari post-test mengalami peningkatan yakni menjadi 78%. Hal ini menunjukkan tingkat keberhasilan siswa dalam memecahkan persoalan mengalami peningkatan yang baik.

Peningkatan High Order Thinking Skill Siswa Melalui Pendampingan Computational Thinking Ferdi Chahyadi; Martaleli Bettiza; Nola Ritha; Muhamad Radzi Rathomi; Nurul Hayaty
Jurnal Anugerah Vol 3 No 1 (2021): Jurnal Anugerah: Jurnal Pengabdian kepada Masyarakat Bidang Keguruan dan Ilmu Pen
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Maritim Raja Ali Haji

Persaingan global yang dihadapi saat ini, menuntut adanya perubahan di dalam pembelajaran agar kecakapan dan keterampilan anak didik semakin berkembang. Kemampuan literasi matematika menjadi salah satu yang harus dimiliki para siswa dalam menghadapi tantangan global tersebut. Kegiatan pelatihan dan pendampingan Computational Thinking dengan menerapkan High Order Thinking Skill (HOTS) yang dilakukan diharapkan dapat menambah wawasan siswa terhadap pemahaman dalam melakukan problem solving. Serta, menumbuhkan kreativitas siswa, budaya informasi, algoritma dan berpikir komputasional dalam menyelesaikan suatu permasalahan dalam bentuk tantangan yang dikenal dengan nama Bebras Challenge. Dalam tahapan pelaksanaannya dilakukan tahapan-tahapan yakni pre-test, pelatihan & pendampingan, serta post-test. Pre-test terhadap 15 siswa menunjukkan rerata siswa dalam menjawab soal secara benar adalah sebanyak 60%. Pelatihan-dan pendampingan dilakukan melalui aplikasi daring. Pertemuan dilaksanakan sebanyak 5 kali pertemuan. Sedangkan hasil dari post-test mengalami peningkatan yakni menjadi 78%. Hal ini menunjukkan tingkat keberhasilan siswa dalam memecahkan persoalan mengalami peningkatan yang baik.

Optimasi Pemilihan Takjil Berbasis Multi-Attribute Decision Making dengan Model Yager Muhamad Radzi Rathomi; Ritha, Nola; Hayaty, Nurul
JISTech : Journal of Information Systems and Technology Vol. 2 No. 1 (2025): Juni 2025
Publisher : Perhimpunan Ahli Teknologi Informasi dan Komunikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71234/jistech.v2i1.52

The choice of takjil as an iftar dish is frequently decided subjectively, neglecting certain elements that could affect the optimality of the decision. This study employs Multi-Attribute Decision Making (MADM) utilizing the Yager Model to identify the optimal takjil alternative based on established criteria. The five primary criteria employed in this analysis are flavor, cost, nutritional value, accessibility, and feasibility. The calculating method initiates with data standardization, weight allocation, and the implementation of the Yager Model to derive the preference value for each choice. Of the seven evaluated alternatives, the findings demonstrate that Kolak Pisang is the most advantageous option, attaining the maximum minimum value of 0.919. Consequently, this strategy serves as a more rational and objective means of selecting takjil that corresponds with consumer preferences and requirements. The utilization of the Yager Model in alternative contexts offers prospects for additional research in multi-criteria decision-making

Implementasi Metode VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) dalam Pengambilan Keputusan Penerima Bantuan Langsung Tunai Dana Desa di Daerah Pesisir (Studi Kasus : Pengujan, Bintan) Cut Putri Khairani; Nola Ritha; Nurul Hayaty
Sustainable Vol 12 No 2 (2023): Jurnal Sustainable : Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/60cyjp45

Inappropriate distribution of assistance is one of the problems often encountered in the provision of social assistance program benefits, such as the Village Fund Cash Assistance (BLT-DD) in Pengujan Village. There are still some people who are marked as recipients of direct cash assistance but not in accordance with the predetermined criteria. This research implements a decision support system method with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method in making decisions on BLT-DD recipients. The VIKOR method selects and ranks alternatives based on conflicting criteria. In this method, alternatives are evaluated based on all the criteria set, choosing a solution that is close to the ideal. This research involved analyzing 200 community data in Pengujan Village, Bintan. The criteria evaluated included age, occupation, income, dependents, house type & status, fishing needs, fishing vehicle, history of illness, disability, and history of receiving assistance. The testing methods used in this research include user acceptance testing, precision, recall, and accuracy. Testing was carried out through filling out questionnaires and using desktop devices, involving 10 respondents from village officials and neighborhood associations in Pengujan Village. Based on the analysis conducted, the test results with a precision value of 71%, recall of 71%, and accuracy of 94%. In addition, user acceptance testing also achieved a feasibility value of 88%.

Klasifikasi Jenis Lamun Menggunakan Ekstraksi Fitur GLCM dan Algoritma K-Nearest Neighbor (KNN) M. Mudaffarsyah; Muhammad Azza Al Kausar; Obi Luter Sihombing; Halta Putra Ash Sidiq; Kirana Putri Fercia; Nurul Hayaty
Sustainable Vol 13 No 2 (2024): Jurnal Sustainable : Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/nzpd9n52

Seagrass is a type of flowering plant (Angiospermae) that grows fully submerged in shallow coastal waters and estuaries, playing a vital role in marine ecosystems. Currently, seagrass species identification is still performed manually by experts, which is time-consuming, costly, and labor-intensive. To support more efficient conservation and ecological monitoring, an automated, fast, and accurate method is needed. This study proposes the combination of the K-Nearest Neighbors (KNN) algorithm for classification and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. The seagrass image data was obtained from the Roboflow website, and the value of k used in KNN was set to 3. Feature extraction using GLCM was conducted at angles of 0°, 45°, 90°, and 135°. The results showed the highest accuracy at k=3, with 77.42% accuracy on training data and 73.33% on testing data. Therefore, the combination of KNN and GLCM has proven capable of providing fairly accurate results in identifying seagrass species.

Klasifikasi Jenis Gonggong Melalui Pendekatan Pengenalan Objek Berbasis MobileNet-SSD Noval, Muhammad; M Afief Anugrah; Faiz Arrafi; Ridho Ramadhan; Marcel Wangnandra; Nurul Hayaty
Sustainable Vol 13 No 2 (2024): Jurnal Sustainable : Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/pnrc3w93

Gonggong is a type of sea snail that is commonly consumed and has become an icon of the culinary specialties of the Riau Islands Province. The most commonly recognized and consumed types in the Riau Islands are Laevistrombus turturella and Strombus canarium. These two types of gonggong have similar physical characteristics and can be difficult to distinguish. Therefore, this research was conducted to find a practical solution that can classify types of gonggong based on their visual images. This study uses a real-time object detection approach based on the MobileNet SSD framework implemented in TensorFlow and applied to an Android-based mobile application. The dataset used consists of 418 images of both types of gonggong that have been augmented with and without backgrounds. The results of the tests show that the model has a confidence level of 83% for images without backgrounds, and 67% for images with backgrounds. These findings indicate that the method used has the potential for further development to improve the model's confidence level in classifying types of gonggong.

KLASIFIKASI JENIS POHON MANGROVE BERDASARKAN CITRA DAUN MENGGUNAKAN METODE K-NEAREST NEIGHBOUR (KNN) Irfan Ibrahim; Maulana Fitra Ramadhani; Muhammad Ridho; M. Wisnu Adjie Pramudya; Putri Suci Renita; Apriliani Putri; Nadia Ayu Putri Priyani; Seffi Rozahana; Adinda; Nurul Hayaty
Sustainable Vol 13 No 2 (2024): Jurnal Sustainable : Jurnal Hasil Penelitian dan Industri Terapan
Publisher : Fakultas Teknik Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/h45hyv18

Studi ini dilakukan untuk mengimplementasikan algoritma KNN (K-Nearest Neighbour) dalam klasifikasi bakau menggunakan citra daun. Penelitian ini menggunakan 1.550 data citra daun Mangrove dengan menggunakan python dibagi menjadi empat kelas oleh Avicennia alba, Bruguiera gymnorrhiza, Rhizophora apiculata dan Sonneratia alba. Tingkat keberhasilan klasifikasi yang dicapai oleh sistem menggunakan metode K-Nearest Neighbour mencapai 93,75% dengan nilai k = 3. Hasil penelitian ini menunjukkan bahwa model KNN bisa mengklasifikasi jenis Avicennia alba dan Sonneratia alba dengan jelas, namun terdapat sedikit kesalahan dalam spesies Bruguiera gymnorrhiza dan Rhizophora apiculata karena memiliki kemiripan ciri tekstur antara satu dengan yang lain.

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