Claim Missing Document
Check
Articles

ALAT PEMISAH WARNA OBJEK BERBASIS MIKROKONTROLER Afrillia, Yesy
Jurnal Teknologi Terapan and Sains 4.0 Vol 1 No 2 (2020): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v1i2.3254

Abstract

Pembuatan alat pemisah warna objek yang dapat memindahkan objek warna dari satu  tempat  ke  tempat lain dengan warna yang telah disesuaikan dari rangkaian tersebut  apabila objek  yang berwarna di letakkan pada corong kemudian turun mengenai sensor TCS3200,  maka  secara otomatis akan memberikan tegangan input ke mikrokontroler, sehingga mikrokontroler akan bekerja dan membaca program yang tersimpan, kemudian  mikrokontroler  akan  memberikan  output  tegangan ke motor servo untuk  menggerakan  objek warna ,dan menjalankan program secara otomatis dimana servo akan  memindahkan objek warna dari tempat sensor ke tempat wadah yang sesuai dengan warna yang  sama  yang  telah di sediakan, alat yang akan dibuat menggunakan mikrokontroler sebagai otak  pengendalinya. Struktur serta antar muka mikrokontroler  yang  sederhana  memberikan  kemudahaan  pengguna  dalam  memahaminya, dan dalam kaitannya dengan penyortiran ini  akan  dibuat  secara  sistematis  dan teliti dimana akan menyortir barang berupa objek warna yang pengendaliannya dan pendeteksian melalui sensor secara otomatis.Kata Kunci : Sensor Warna TCS3200, Mikrokontroler, Motor Servo
PEMBANGUNAN GEDUNG FAKULTAS AKSI-ADB 2020 YANG MENGIMPLEMENTASIKAN TOILET BERBASIS GENDER DAN PENTINGNYA PERLINDUNGAN TENAGA KERJA DI LINGKUNGAN UNIVERSITAS MALIKUSSALEH Fithra, Herman; Sofyan, Sofyan; Sarana, David; Mukhlis, Mukhlis; Siska, Deassy; Afrillia, Yesy
Jurnal Teknologi Terapan and Sains 4.0 Vol 3 No 2 (2022): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v3i2.8274

Abstract

Permasalahan ketidakadilan gender bisa ditemukan dalam beragam hal dalam keseharian, salah satunya adalah toilet. Di berbagai tempat hiburan maupun ruang publik lain seperti sekolah dan stasiun, jumlah toilet untuk perempuan bisa dibilang kurang memadai. Universitas Malikussaleh melalui proyek AKSI ADB membangun 7 gedung Fakultas masing-masing 2 lantai dengan penempatan Toilet yang sudah memasukkan unsur gender didalamnya. Permasalahan toilet perempuan ini tidak lepas dari pandangan yang responsive gender yang saat ini sedang marak di masyarakat, termasuk di kalangan ilmuwan dan akademisi. Dalam tahap awal, pandangan ini mengesampingkan fakta biologis bahwa perempuan mempunyai kebutuhan unik sehubungan dengan pengalaman mandi dan bersih menstruasi mereka. Pengalaman ini sangat berpengaruh terhadap waktu yang perempuan habiskan di toilet. Sebuah studi dari Science Daily tahun 2017 pernah menyebutkan, perempuan menghabiskan 50 persen waktu lebih lama dari laki-laki di toilet. Hal ini bisa ditambah faktor sedikitnya jumlah bilik toilet sehingga perempuan mesti mengantre lebih lama di sana. Melalui Peraturan Menteri kesehatan Republik Indonesia nomor 48 tahun 2016 tentang standar keselamatan dan kesehatan kerja perkantoran, Universitas Malikussaleh membangun Toilet yang responsive gender dengan rasio perbandingan toilet 1:25 untuk perempuan dan 1:40 untuk laki-laki yang ada di dalamnya.Keywords: Gender, Rasio Toilet, Responsive
Prediksi Kesehatan Mental Remaja Berdasarkan Faktor Lingkungan Sekolah Menggunakan Machine Learning Rahma, Mutiara; Fikry, Muhammad; Afrillia, Yesy
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8556

Abstract

Adolescent mental health is a crucial aspect that affects academic performance, social relationships, and overall well-being. The school environment is one of the primary factors influencing adolescents' mental conditions. This study aims to predict adolescent mental health levels based on school environmental factors using the Random Forest algorithm. Data were collected from 229 adolescents in Lhokseumawe and categorized into four classes of mental health conditions. The research methodology includes data preprocessing, model training, and performance evaluation using accuracy and other relevant metrics. The results show that the model achieved an accuracy of 80.43%, with the highest F1-score of 0.90 in the category indicating no mental health issues. Feature importance analysis identified loneliness, feelings of worthlessness, academic pressure, and home-related stress as the most influential factors in the predictions. While the model effectively classified most data, some misclassifications occurred at certain mental health levels. Thus, the Random Forest model proves to be an effective predictive tool for detecting potential adolescent mental health issues. The findings of this study can serve as a reference for educational institutions in designing more targeted intervention strategies to support adolescent mental well-being.
Public Facility Recommendation System in Subulussalam City Using Fuzzy C-Means Algorithm Berutu, Indah Fachlira; Dinata, Rozzi Kesuma; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.873

Abstract

Subulussalam City, as one of the autonomous regions in Aceh Province, Indonesia, has excellent potential to develop public facilities to improve the quality of life for its residents. Recommendation systems have become an effective solution in helping users find relevant information based on the preferences and needs of the community. This research focuses on developing a recommendation system using the Fuzzy C-Means algorithm. This algorithm is one of the clustering methods capable of handling uncertainty and ambiguity in data. This study aims to develop and analyze a public facility recommendation system in Subulussalam City using the Fuzzy C-Means algorithm. The dataset in this study was obtained from the Youth, Sports, and Tourism Office of Subulussalam City and the results of a research questionnaire. Regarding the names of each public facility, it provides information about the location and various forms of visitor assessments, including evaluations related to accessibility, facilities, costs, environment, and visitor experiences, using a rating scale of 1-5. Based on the testing results, the Fuzzy C-Means clustering algorithm can group facilities based on characteristics and user preferences, resulting in more personalized and relevant recommendations. The data to be clustered is divided into two categories: recommended and not recommended. The study's results using the Fuzzy C-Means algorithm show the final grouping based on the degree of membership from the last iteration of each public facility, with cluster 1 containing 31 locations and cluster 2 containing 31 locations.
Classification Of Outpatient Visit Status Walking at Dr. Zubir Mahmud Hospital Using Algoritma C4.5 Fikria, Putri; Dinata, Rozzi Kesuma; afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.865

Abstract

This study aims to classify the status of outpatient visits at RSUD Dr. Zubir Mahmud into three main categories, namely "Very Urgent", "Urgent", and "Not Urgent”, using the C4.5 algorithm. The web-based system uses the PHP programming language and MySQL database to ensure ease of implementation and efficient data management. The classification process is done by setting threshold parameters, calculating entropy, and the gain ratio to form an accurate and reliable decision tree. The results show that the C4.5 algorithm can classify patient visit data with a reasonably high accuracy rate, which is 93.75% for 2022 data and reaches 100% for 2023 data. In 2022 the “Very Urgent" category had 9 True Positives (TP); in 2023, the number remained consistent. However, in both years, there were also False Negatives in the same category, with 4 cases in 2022 and 5 cases in 2023. The "Urgent" and "Not Urgent" categories show suboptimal classification performance due to uneven data distribution, which causes the precision and recall values in these categories low. Model evaluation was conducted using evaluation metrics such as precision, recall, and F1 score. The evaluation results show that the model works very well in identifying high-priority categories, but further development is needed to improve classification in other categories. This system is expected to be a reliable tool in decision-making in health services, especially in determining the priority of patient services appropriately and efficiently. With further development, this system has the potential to be widely applied in various other hospitals.
Performance Analysis of API Protocol Models as Recommendations for Developers in Application Development Fhonna, Rizky Putra; Afrillia, Yesy; Ilhadi, Veri; Arif, Abdul Halim; Selian, Riko Ardiansyah
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav3041

Abstract

The evaluation of various API types reveals distinct strengths and weaknesses. REST APIs exhibit inefficient performance with high average response times and an error rate of approximately 14%, indicating potential delays and instability under load. SOAP APIs, with an average response time of 167 ms, perform better than REST in terms of speed but still lag behind GraphQL and have a slightly higher error rate of 14.80%. GraphQL demonstrates the fastest average response time at around 171 ms, offering high efficiency in data delivery, although its error rate is notably high at 15%, signaling a need for improved stability. RPC APIs, with an average response time of 238 ms, are less speedy compared to GraphQL and SOAP but excel in stability with a very low or zero error rate, making them highly reliable under high loads. Overall, GraphQL is optimal for applications requiring rapid data interaction, RPC is best suited for scenarios demanding high consistency and reliability, SOAP offers a middle ground, and REST may be appropriate for simpler, less demanding applications.
Expert System For Detecting Soil Fertility Levels for Oil Palm Cultivation Using the Fuzzy Tsukamoto Yanti, Winda; Yunizar, Zara; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.884

Abstract

Soil fertility is one of the critical factors that affect the productivity of oil palm plants. Inappropriate soil fertility levels can cause suboptimal plant growth and even crop failure. Low public knowledge about soil fertility is also a significant factor. This research aims to build an expert system that can detect the soil fertility level for oil palm plants using the fuzzy Tsukamoto method. This system uses three main parameters as a reference: soil acidity (pH), soil moisture, and soil texture. The fuzzy Tsukamoto method was chosen because it can handle uncertain data and provide more flexible results. The system was developed web-based using the PHP programming language and MySQL database, and tested on 49 soil data points from the Agricultural Extension Center of Matangkuli District, North Aceh Regency. The system successfully detected soil fertility levels accurately and consistently. Tests were conducted on 49 soil sample data from various villages in Matangkuli District, North Aceh Regency, where soil fertility in the Low category was found in 43 villages with a percentage of 84%, soil fertility in the Medium category was found in 6 villages with a rate of 16% and soil fertility in the High category was not found in any town of Matangkuli District with a percentage of 0% with valid fertility classification results and by expert judgment. With this system, farmers and agricultural extension workers can be helped to make the right decisions regarding the feasibility of land for planting oil palm plants.
Job Vacancy Recommendation System using JACCARD Method On Graph Database Riza, Saiful; Fuadi, Wahyu; Afrillia, Yesy
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2387

Abstract

In the rapidly evolving digital era, recommendation systems play a crucial role in helping users discover relevant information aligned with their preferences. PT Nirmala Satya Development, a company engaged in psychology and human resource development, faces challenges in utilizing big data consisting of 500 applicants, 500 job postings, and 500 job applications to generate accurate and relevant job recommendations. This study develops a job recommendation system using the Jaccard Coefficient method to measure similarity between users based on their job application history, implemented within a Neo4j graph database. The system models the relationships between entities through nodes and edges, allowing dynamic analysis using the Cypher Query Language. Testing on 237 users demonstrated that the majority received at least one relevant recommendation, with recall values often reaching 1.0, especially among users who had a single job target. The system achieved precision values ranging from 10% to 20%, which is considered acceptable given that ten recommendations are generated per user. The highest F1-score reached 0.33, although some users received F1 = 0 due to limited application history or unique preferences. Overall, the system effectively delivers personalized and efficient job recommendations, particularly for active users. This research also proves that combining the Jaccard Coefficient with a graph database structure is a powerful approach to representing and analyzing complex relationships between users and job postings in a modern recruitment platform.
Detection of Qur’anic Ikhfa Patterns in Digital Images Using Binary Similarity Distance Measures (BSDM) with 3W-Jaccard Formula Julianansa, Ririn; Fadlisyah; Yesy Afrillia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9814

Abstract

Recitation rules in the Qur'anic script form various visual patterns. One of the selected rules for this study is the Ikhfa pattern. Ikhfa is a recitation rule pronounced subtly when the nun sukun (نْ) or tanwin (ـَــًـ, ـِــٍـ, ـُــٌـ) is followed by one of 15 specific letters, namely: ta’ (ت), tsa’ (ث), jim (ج), dal (د), dzal (ذ), za’ (ز), sin (س), syin (ش), shad (ص), dhad (ض), tha’ (ط), zha’ (ظ), fa’ (ف), qaf (ق), and kaf (ك). In this study, the primary challenge is the difficulty of automatically detecting the Ikhfa pattern in both digital and printed Qur'anic texts. This challenge arises from the subtlety of the recitation rule, which makes it difficult to distinguish from other recitation patterns. To address this, the Ikhfa pattern is detected using image processing techniques, and pattern classification is performed using the Binary Similarity and Distance Measures (BSDM) method. The results indicate that the pattern detection system, employing BSDM with the 3W-Jaccard formula, achieved a detection rate of 83.84%. This suggests that the 3W-Jaccard formula is an effective approach for detecting similar recitation patterns. One advantage of the 3W-Jaccard formula is its ability to recognize patterns with a relatively small amount of reference data, making it highly suitable for implementation in the detection system.
Stunting Risk Detection and Food Recommendation via Maternal Diagnosis Using the CF Method Kautsar, Al; Asrianda, Asrianda; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9949

Abstract

Stunting in children often stems from maternal health conditions during pregnancy. This study aims to develop an intelligent rule-based IF–THEN system using the Certainty Factor method as a decision-support tool for the early detection of stunting risk factors. The detection is performed indirectly by diagnosing maternal health conditions during pregnancy. The knowledge base was constructed through interviews with obstetricians and nutritionists, encompassing 20 symptoms categorized into three primary conditions namely Chronic Energy Deficiency (CED), anemia, and preeclampsia. A total of 119 pregnant women from 11 villages in Muara Satu District participated as respondents. Implementation results revealed that among the respondents, 20 were identified with CED, 96 had anemia, and 3 exhibited signs of preeclampsia. Based on Certainty Factor (CF) calculations, the confidence distribution for CED included 2 respondents with CF <50%, 5 respondents within the 50–80% range, and 13 respondents with CF >80%. For anemia, 1 respondent had a CF value <50%, 4 fell within the 50–80% range, and 91 respondents had CF values above 80%. Meanwhile, for preeclampsia, all respondents exceeded the 50% CF threshold, with 1 respondent in the 50–80% range and 2 respondents >80%. In addition to diagnosis, the system provides tailored meal recommendations (breakfast, lunch, and dinner) based on the identified health conditions. Expert validation indicated a 90% agreement rate. However, results still require confirmation through clinical examinations and consultations to ensure medical accuracy.