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All Journal TEKNIK INFORMATIKA Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Fadhilah Syafria
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Penerapan Metode Clustering Dengan K-Means Untuk Memetakan Potensi Tanaman Padi di Sumatera Irma Sanela; Alwis Nazir; Fadhilah Syafria; Elin Haerani; Lola Oktavia
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4523

Abstract

Rice plants are the primary source of rice, the staple food for the majority of the Indonesian population. Despite the presence of other food alternatives, rice remains irreplaceable for those accustomed to consuming rice. According to data from the Food and Agriculture Organization of the United Nations (FAO) in 2018, Indonesia is the third-largest rice producer in the world, with a total production of 59.2 million tons. However, urban and agricultural spatial planning is not yet fully integrated, resulting in often conflicting decisions in land use planning for agriculture and urban development. To meet the rice demand in Sumatra, efforts are needed to increase rice production in each province. Therefore, this research aims to map the potential for rice cultivation in Sumatra based on production and harvest results from 1993 to 2020. The method used in this study is K-Means, which allows the grouping of rice potential areas into three categories: high, medium, and low. The research results produced three clusters, evaluated using the Davies Bouldin Index (DBI) with a value of 0.3943. The clustering results indicate that Cluster 0 contains 92 areas with a high success rate, Cluster 2 comprises 84 areas with a medium success rate, and Cluster 1 consists of 48 areas with a low success rate. The category of low success rate is found in Cluster 1 with 48 areas. Cluster 0 includes Aceh, North Sumatra, West Sumatra, South Sumatra, and Lampung within certain time periods. Cluster 1 encompasses other areas with different characteristics. Cluster 2 includes the provinces of Riau, Jambi, and Bengkulu.
IMPLEMENTASI K-MEANS CLUSTERING PADA DATA PENGELOMPOKAN PENDAFTARAN MAHASISWA BARU (STUDI KASUS UNIVERSITAS ABDURRAB Muhammad Hanif Abdurrohman; Elin Haerani; Fadhilah Syafria; Lola Oktavia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 1 (2024): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i1.4255

Abstract

Menghadapi dinamika kompleks pendaftaran mahasiswa baru, metode k-means clustering diperkenalkan sebagai pendekatan utama. Fokusnya adalah pada Universitas Abdurrab, di mana diselidiki berbagai atribut calon mahasiswa, termasuk jenis kelamin, pendidikan orangtua, penghasilan orangtua, kota/kabupaten asal, provinsi, usia, dan pilihan program studi. Dengan algoritma k-means clustering, tujuan penelitian adalah mengungkap pola yang mendasari preferensi dan karakteristik kelompok mahasiswa baru. Hasil dari penelitian ini memberikan wawasan mendalam mengenai faktor-faktor yang mempengaruhi keputusan penerimaan mahasiswa baru di lingkungan kampus Universitas Abdurrab. Pada penelitian ini Davies-Bouldin Index (DBI) digunakan sebagai metode untuk menentukan jumlah cluster optimal, nilai DBI terendah adalah 1,5 yang terjadi pada 8 cluster. Hal ini menunjukkan bahwa 8 cluster adalah jumlah cluster optimal untuk data yang telah ditransformasi dan siap dilakukan k-means clustering. Setelah melaksanakan proses clustering dengan metode K-Means yang melibatkan pembentukan 8 cluster, untuk menemjukan pola dan wawasan dari hasil clustering, ada dua cara yang digunakan pada penelitian ini, pertama membuat heatmap korelasi fitur yang ditampilkan, dapat diperoleh informasi mengenai hubungan antar variabel. Nilai korelasi berkisar antara -0.4 hingga 1.0 dimana nilai positif menunjukkan korelasi positif dan nilai negatif menunjukkan korelasi negatif. Korelasi positif berarti bahwa jika nilai salah satu variabel meningkat, maka nilai variabel lainnya juga cenderung meningkat. Sebaliknya, korelasi negatif berarti bahwa jika nilai salah satu variabel meningkat, maka nilai variabel lainnya cenderung menurun.
Penerapan Learning Vector Quantization 3 Dalam Menentukan Bakat Anak Agung Syaiful Rahman; Elvia Budianita; Reski Mai Candra; Fadhilah Syafria
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4398

Abstract

Abstrak - Banyaknya bakat anak yang tidak diketahui oleh sebagian besar Orang tua di Indonesia dikarenakan sedikitnya ahli anak sebagai tempat untuk konsultasi yang menjadi faktor utama dalam perMasalahan ini. Tujuan dari penelitian ini ialah agar para Orang tua dapat mempermudah dalam menggali potensi dalam diri anak mereka masing-masing, yakni dengan menggunakan jaringan saraf tiruan. Ada beberapa metode dalam jaringan saraf tiruan,  learning vector quantization 3 merupakan saah satu dari bagian tersebut. Bakat anak yang diambil merupakan bakat anak yang berdasarkan standar United State of Education America. Anak yang diteliti merupakan murid dari Sekolah Dasar Negeri 011 Titian Resak dengan rentang usia 10-12 tahun. Penelitian ini menunjukkan bahwa learning vector quantization 3 membutuhkan sedikitnya 5 kriteria dengan 30 variabel bakat anak sebagai dasar dari penelitian ini. Berdasarkan hasil yang didapatkan, sistem ini berhasil mengidentifikasi bakat anak dengan rentang pembagian 90% data latih dan 10% data uji dan parameter window (0.1,0.2,0.3), epsilon (0.1,0.2,0.3), alpha (0.1) sebesar 81.82%.Kata kunci : Bakat Anak, Learning Vector Quantization 3, Jaringan Saraf Tiruan Abstract - The number of children's taents that are not known by most parents in Indonesia is due to the lack of child experts as a place for consultation which is the main factor in this problem. The purpose of this research is that parents can make it easier to explore the potentia in their respective children, namely by using artificia neura networks. There are severa methods in artificia neura networks, learning vector quantization 3 is one of them. The taent of the child taken is the child's taent based on the standards of the United State of Education America. The children studied were students from the 011 Titian Resak State Elementary School with an age range of 10-12 years. This study shows that learning vector quantization 3 requires at least 5 criteria with 30 variables of children's taents as the basis of this research. Based on the results obtained, this system succeeded in identifying children's taents with a distribution range of 90% of training data and 10% of test data and parameters window (0.1.0.2.0.3), epsilon (0.1.0.2.0.3), apha (0.1) of 81.82% .Keyword : Child Talent, Learning Vector Quantization 3, Artificia Neura Network
Penerapan Algoritma Apriori Dalam Menentukan Pola Perilaku Dan Gaya Hidup Terhadap Penderita Hipertensi Hara Novina Putri; Elvia Budianita; Fadhilah Syafria; Fitri Insani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4402

Abstract

Abstrak - Data mining merupakan teknik menggali informasi baru dari gudang data, informasi sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah mencapai sebuah tujuan, hal ini membuat setiap orang berlomba untuk memperoleh informasi, demikian juga pada bidang kesehatan terkhusus yang diteliti penulis yaitu penderita hipertensi. Hipertensi merupakan penyakit tidak menular yang prevalensinya sebesar 22% pada kelompok usia  18 tahun pada 2014 dan terus meningkat serta mampu meningkatkan risiko penyakit jantung koroner sebesar 12% dan meningkatkan risiko stroke sebesar 24%. Kebanyakan gejala yang dialami penderita tidak dapat dideteksi secara dini. Karenanya, perlu dilakukan penelitian dalam mendiagnosa pola perilaku dan gaya hidup terhadap penderita hipertensi menggunakan metode algoritma apriori. Data yang didapatkan melalui penyebaran kuisioner di puskesmas Melur dan rumah sakit Aulia Hospital. Atribut yang digunakan pada penelitian ini adalah jenis kelamin, usia, kebiasaan merokok, kebiasaan mengkonsumsi alkohol, intensitas aktifitas fisik, olahraga, dan pola konsumsi makanan. Pada pengujian parameter algoritma apriori dalam mencari pola dengan melihat hasil nilai support dan confidence pada metode algoritma apriori. Pengujian penelitian ini menggunakan tools Tanagra versi 1.4. Dari pengujian 300 data penderita hipertensi menggunakan nilai support 30% dan confidence 85% ditemukan 6 pola/rules dengan lift ratio ≥1.Kata kunci: Hipertensi, Diagnosa, Algoritma apriori, support, confidence, lift ratio Abstract - Data mining is a technique to dig new information from the data warehouse, information is very important and valuable because by mastering information, it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in the field of health, especially those studied by the author, namely people with hypertension. Hypertension is a non-communicable disease whose prevalence was 22% in the age group of ≥ 18 years in 2014 and continues to increase and is able to increase the risk of coronary heart disease by 12% and increase the risk of stroke by 24%. Most of the symptoms experienced by sufferers cannot be detected early.Therefore, it is necessary to conduct research in diagnosing behavioral patterns and lifestyles for hypertension patients using the a priori algorithm method. The data obtained through the distribution of questionnaires at the Melur health center and Aulia Hospital. The attributes used in this study were gender, age, smoking habits, alcohol consumption habits, intensity of physical activity, exercise, and food consumption patterns. In testing the parameters of the a priori algorithm, it is produced in looking for patterns by looking at the results of support and confidence values in the a priori algorithm method. Testing this study using Tanagra tools version 1.4. From testing 300 data on hypertension patients using support values of 30% and confidence of 85% found 6 patterns / rules with an lift ratio of ≥1.Keywords: Hypertension, Diagnosis, Apriori algorithm, support, confidence, lift ratio
Penerapan Algoritma Random Forest Untuk Analisis Sentimen Komentar Di YouTube Tentang Islamofobia Ibnu Afdhal; Rahmad Kurniawan; Iwan Iskandar; Roni Salambue; Elvia Budianita; Fadhilah Syafria
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 1 (2022): Februari 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i1.4004

Abstract

Abstrak - Islamofobia adalah bentuk prasangka, intimidasi, kebencian dan ketakutan terhadap agama Islam dan orang Muslim. Stigma islamofobia muncul karena adanya suatu kejadian pengeboman atau teror lainnya yang dihubungkan dengan Islam.  Komentar yang mengarah ke islamofobia banyak dijumpai pada media sosial youtube. Islamofobia di internet merupakan salah satu bentuk kekerasan verbal. Oleh karena itu, komentar pengguna terkait suatu kejadian pengeboman atau teror berpotensi untuk dianalisis sebagai bentuk kepedulian dalam mencegah kekerasan verbal. Tetapi analisis secara manual sulit dilakukan dan memerlukan waktu yang lama. Algoritma pada pembelajaran mesin dapat digunakan untuk melakukan analisa sentimen dengan cepat. Algoritma yang digunakan pada penelitian ini adalah random forest. Berdasarkan studi pustaka, algoritma random forest dapat menghasilkan ketepatan yang tinggi. Penelitian ini menggunakan 1000 data komentar di youtube berbahasa Indonesia terkait video yang menampilkan suatu kejadian pengeboman atau teror. Berdasarkan hasil analisis, terdapat 631 komentar positif dan 369 komentar negatif atau mengandung islamofobia. Berdasarkan eksperimen, algoritma random forest menghasilkan akurasi mencapai 79%. Algoritma random forest dianggap baik dalam melakukan klasifikasi sentimen dengan cepat.Kata kunci: analisis sentimen, islamofobia, random forest, youtube Abstract - Islamophobia is a form of prejudice, intimidation, hatred, and fear of Islam and Muslims. The stigma of Islamophobia arises because of bombing or other terror associated with Islam. Comments that lead to Islamophobia are often found on social media youtube. Islamophobia on the internet is a form of verbal violence. Therefore, user comments related to a bombing or terror incident have the potential to be analyzed as a form of concern in preventing verbal violence. However, manual analysis is difficult and takes a long time. Algorithms in machine learning can be used to perform sentiment analysis quickly. The algorithm used in this study is a random forest. The random forest algorithm can produce high accuracy based on the literature study. This study obtained 1000 comments data on youtube in Indonesian related to videos showing a bombing or terror incident. Based on the analysis results, there were 631 positive comments and 369 islamophobia  i.e., negative comments. Based on experiments, the random forest algorithm produces an accuracy of 79%. The random forest algorithm is considered good in doing sentiment classification quickly.Keywords—islamophobia, random forest, sentiment analysis, Youtube
Analisis Sentimen Komentar Di YouTube Tentang Ceramah Ustadz Abdul Somad Menggunakan Algoritma Naïve Bayes Habibi Al Rasyid Harpizon; Rahmad Kurniawan; Iwan Iskandar; Roni Salambue; Elvia Budianita; Fadhilah Syafria
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 1 (2022): Februari 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i1.4008

Abstract

Abstrak - Sosial media tidak hanya digunakan oleh masyarakat Indonesia untuk hiburan, tetapi juga sebagai media edukasi. Youtube merupakan salah satu media sosial yang terkenal di Indonesia dengan 93,8% pengguna. Youtube juga dimanfaatkan sebagai media Dakwah seperti yang dilakukan oleh Ustadz Abdul Somad. Ustadz Abdul Somad merupakan ulama yang berpengaruh di Indonesia. Beliau sering mengunggah video yang membahas berbagai jenis persoalan agama khususnya pada bidang hadist dan fiqih. Pengguna Youtube dapat memberikan feedback berupa like, dislike dan komentar terhadap video yang ditayangkan. Feedback diperlukan oleh pembuat konten di Youtube untuk melihat tanggapan pengguna. Analisa secara manual sulit dilakukan karena jumlah data yang besar. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap Ustadz Abdul Somad melalui  komentar youtube menggunakan algoritma Naïve Bayes. Penelitian ini menggunakan 1000 komentar dari 10 video yang ada di Youtube mengenai Ustad Abdul Somad. Naïve Bayes merupakan algoritma yang sederhana, namun memiliki akurasi yang tinggi dan dapat digunakan pada data yang sedikit. Berdasarkan hasil penelitian, didapatkan sebanyak 67% berkomentar positif, 27% berkomentar netral  dan 6% berkomentar negatif. Berdasarkan pengujian didapatkan akurasi sebesar 87%, presisi 91% dan recall 97%. Berdasarkan pengujian tersebut dapat disimpulkan bahwa penelitian ini dapat digunakan untuk hasil sentimen dengan cepat di Youtube.Kata kunci: Analisis Sentimen, Naïve Bayes, Ustadz Abdul Somad, Youtube Abstract - Indonesian people have been used Youtube for entertainment and as an education. As Indonesia's most popular social media, Youtube has 93.8% users. YouTube is also used as a medium of Da'wah, like Ustadz Abdul Somad. Ustadz Abdul Somad is an influential Preacher in Indonesia. He often uploads videos that lecture various types of religious issues, especially in the fields of hadith and fiqh. YouTube users can provide feedback in the form of likes, dislikes, and comments on videos that are shown. Creators need feedback on YouTube to see user feedback. Manual analysis is complicated because of the large amount of data. Therefore, this study aimed to analyze public sentiment towards Ustadz Abdul Somad through YouTube comments using the Naïve Bayes algorithm. This study obtained 1000 comments from 10 videos about Ustad Abdul Somad. Naïve Bayes is a simple algorithm with high accuracy and can be used on small data. Based on the results, it was found that 67% commented positively, 27% commented neutrally, and 6% commented negatively. Based on the experimental testing, the accuracy is 87%, precision is 91%, and recall is 97%. Based on these tests, it can be concluded that this research can be used for quick sentiment results on YouTube.Keywords: Sentiment Analysis, Naïve Bayes, Ustadz Abdul Somad, Youtube
Penerapan Metode FP-GROWTH Untuk Analisa Pola Konsumsi Makan Penderita Diabetes Melitus Fratiwi Rahayu; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4401

Abstract

Abstrak - Penyakit diabetes melitus adalah gejala yang timbul terhadap seseorang akibat kadar gula darah yang tinggi atau hiperglikemia. Kemenkes (2018) menyebutkan bahwa faktor yang dapat menyebabkan terjadinya diabetes melitus salah satunya adalah berdasarkan faktor konsumsi makan. Penelitian ini bertujuan untuk menemukan pola makan dari penderita diabetes melitus. Data yang digunakan pada penelitian ini adalah data yang didapat setelah melakukan wawancara dan penyebaran kuesioner pada penderita diabetes melitus di Puskesmas Melur dan Rumah Sakit Aulia Hospital. Adapun atribut yang akan digunakan pada penelitian ini yaitu jenis kelamin, penyakit penyerta, terapi, frekuensi makan, makanan pokok, konsumsi sayur, konsumsi buah, konsumsi protein nabati, protein hewani, konsumsi gula, makanan ringan, makanan instan, dan minuman manis. Penelitian menggunakan algoritma FP-Growth dengan nilai confidence 100% dan minimum support 40%. Tools yang digunakan RapidMiner 9.1 sehingga didapatkan 13 rules. Dari 13 aturan asosiasi yang dihasilkan dapat disimpulkan bahwa penderita diabetes melitus yang mengkonsumsi sayur 1 porsi dalam sehari, konsumsi buah 1x dalam sehari, dan mengkonsumsi makanan instan 3x dalam seminggu maka penderita diabetes melitus merupakan penderita diabetes terkontrol.Kata kunci: Algoritma FP-Growth, Diabetes Melitus, RapidMiner, Support Abstract - Diabetes mellitus is a symptom that arises in a person due to high blood sugar levels or hyperglycemia. The Ministry of Health (2018) stated that one of the factors that can cause diabetes mellitus is based on eating consumption factors. This study aims to find the diet of people with diabetes mellitus. The data used in this study are data obtained after conducting interviews and the distribution of questionnaires in people with diabetes mellitus at the Melur Health Center and Aulia Hospital. The attributes that will be used in this study are gender, comorbidities, therapy, frequency of eating, staple foods, vegetable consumption, fruit consumption, consumption of vegetable protein, animal protein, sugar consumption, snacks, instant foods, and sugary drinks. The study used the FP-Growth algorithm with a confidence value of 100% and a minimum support of 40%. Tools used by RapidMiner 9.1 so that 13 rules are obtained. From the 13 association rules produced, it can be concluded that people with diabetes mellitus who consume 1 serving of vegetables in a day, consume fruit 1x in a day, and consume instant food 3x in a week, people with diabetes mellitus are controlled diabetics.Keywords : FP-Growth Algorithm, Diabetes Mellitus, RapidMiner, Support
Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian Zulastri, Zulastri; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2437

Abstract

The high divorce rate has a negative impact on couples who will file for divorce and also has an extreme impact on children such as psychological disorders of children. The magnitude of the impact of divorce, it is necessary to predict the divorce decision. In this study, the application of the backpropagation method to predict divorce decisions was carried out. The data used is data on divorce decisions from the Pekanbaru Religious Court from 2020 - 2021 totaling 779. The dataset obtained is not balanced with 724 accepted classes and 55 rejected classes, balancing is done by reducing excess classes. The parameters used in this study build 3 architectural models [6-7-1], [6-9-1], [6-12-1], learning rate (0.01, 0.03, 0.09), max epoch and data sharing (70:30), (80:20), (90:10). The results of this study indicate that the best architectural model is in the network architecture [6-9-1] learning rate 0.09 epoch 300 dataset distribution 80% training data and 20% test data the accuracy value is 80% and the Mean Squared Error (MSE) is 0.1402. In this study, the backpropagation method was successful in predicting divorce decisions.
Klasifikasi American Sign Language Menggunakan Convolutional Neural Network Israldi, Tino; Haerani, Elin; Sanjaya, Suwanto; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2570

Abstract

Communicating is a necessity for all groups or individual because each individual should communicate with their surroundings. Communicating can also make us get information so that it can be used as a reference to be able to adapt. Verbal language used by speaking out loud is a way of communicating with individuals, but not all individuals can communicate with it, especially there are some individuals who have hearing limitations. Because of these limitations, another program that can be used is through sign language. Language requirements are languages that are usually used by people with disabilities in terms of hearing or speaking and sign language also has a fairly well-known sign language standard, namely the American Sign Language (ASL) standard. Unlike languages in the world, sign language is also often of little interest to most people because people's interest in sign language is still lacking so that most people are unable to understand their language. Sign language has many types, one of which is sign language by using hands to form letters and numbers. In overcoming these problems, the solution is to create a system that can be used to recognize sign language, the system developed is a system that used machine learning technology. This study will propose an ASL classification approach through data preprocessing and a convolutional neural network model. The proposed model can classify ASL hand posture images to be translated into the alphabet. The result of this study is an model with accuracy of 99.8% obtained from the process of merging preprocessing data and the convolutional neural network model.
Implementasi Metode Learning Vector Quantization (LVQ) Untuk Klasifikasi Keluarga Beresiko Stunting Aziz, Abdul; Insani, Fitri; Jasril, Jasril; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3478

Abstract

Stunting is a condition where a child's height is too short compared to children of the same age. This condition affects the health of toddlers in the short and long term, such as suboptimal body posture in adulthood, decreased reproductive health, and decreased learning capacity, resulting in suboptimal performance in school. One of the causes of stunting is a lack of nutrition, basic health facilities, and poor parenting practices. However, the current data collection and classification of families at risk of stunting still use Microsoft Excel, which is ineffective in processing large data. Therefore, the LVQ method, which is an improvement of the Vector Quantization method, is used to accelerate the classification process. In this study, 5 parameters were tested, and the optimal result was achieved by using 7 input neurons, Chebychev distance as the distance measure, a learning rate of 0.1, 7 epochs, and 30% of training data. With these parameters, an accuracy of 99.38% was obtained. Based on these results, the LVQ method can help improve accuracy in classifying families at risk of stunting
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Afriyanti, Liza Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Aminuyati Andre Suarisman Aprima, Muhammad Dzaky Ariq At-Thariq Putra Baehaqi Bib Paruhum Silalahi Boni Iqbal Che Hussin, Ab Razak Darmila Dede Fadillah Deny Ardianto Devi Julisca Sari Dina Septiawati Dodi Efendi Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Indrizal, Habibi Putra Inggih Permana Irma Sanela Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Karina Julita Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola Oktavia M Fikry M. Afif Rizky A. Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Maulana Junihardi Mawadda Warohma Mazdavilaya, T Kaisyarendika Mhd. Kadarman Mori Hovipah Mori Hovipah Morina Lisa Pura Muhammad Affandes Muhammad Alvin Muhammad Fahri Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Irsyad Muhammad Syafriandi, Muhammad Muhammad Taufiq Muhammad Yusril Haffandi Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nabyl Alfahrez Ramadhan Amril Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida Rahmad Abdillah Rahmad Abdillah Rahmad Kurniawan Rahmadhani, R. Raja Sultan Firsky Ramadhan, Aweldri Ramadhan, Muhammad Ilham Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Roni Salambue Said Nanda Saputra Satria Bumartaduri Silfia Silfia Siti Ramadhani Siti Sri Rahayu Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Vitriani, Yelvi Wulandari, Fitri Yaskur Bearly Fernandes Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri