cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andy Sapta
Contact Email
saptaandy@gmail.com
Phone
-
Journal Mail Official
lppm_stmik@royal.ac.id
Editorial Address
-
Location
,
INDONESIA
JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Published by Smart Education
ISSN : 26154307     EISSN : 26153262     DOI : -
Core Subject : Economy, Science, Education, Social,
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences
Journal of Science and Social Research is accepts research works from academicians in their respective expertise of studies. Journal of Science and Social Research is platform to disclose the research abilities and promote quality and excellence of young researchers and experienced thoughts towards Change for Development. The journal releases on February and July.
Arjuna Subject : -
Articles 3,645 Documents
 292   293   294   295   296 
IMPLEMENTASI ALGORITMA K-MEANS UNTUK SEGMENTASI PRODUK MAKANAN BERDASARKAN HARGA DAN RATING PADA GOFOOD Irma Sari Dewi Saragih; Zunaida Sitorus
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5736

Abstract

Abstract: This study aims to apply the K-Means algorithm for food product segmentation on the GoFood service based on price and rating variables. The large variety of available products makes data difficult to analyze without systematic grouping, necessitating a method capable of automatically clustering data so that patterns and characteristics become clearer. The K-Means algorithm was chosen for its simple, fast, and effective computational process in dividing data based on distance similarity. The research process includes collecting public datasets, determining the number of clusters, selecting initial centroids, calculating distances using Euclidean Distance, grouping data to the nearest cluster, and iteratively updating centroids until convergence is achieved. Results show that K-Means successfully grouped data into three clusters: Cluster 1 with high price and high rating, Cluster 2 with moderate price and highest rating, and Cluster 3 with low price and very high rating. This study produced a web-based system that automatically processes data and displays clustering results in an easily understandable interface. Keywords: K-Means, Clustering, Segmentation, GoFood, Data Mining Abstrak: Penelitian ini bertujuan menerapkan algoritma K-Means untuk segmentasi produk makanan pada layanan GoFood berdasarkan variabel harga dan rating. Banyaknya variasi produk menyebabkan data sulit dianalisis tanpa pengelompokan sistematis, sehingga diperlukan metode yang mampu mengelompokkan data secara otomatis agar pola dan karakteristik terlihat lebih jelas. Algoritma K-Means dipilih karena memiliki proses perhitungan yang sederhana, cepat, dan efektif dalam membagi data berdasarkan tingkat kemiripan jarak. Proses penelitian meliputi pengumpulan dataset publik, penentuan jumlah cluster, pemilihan centroid awal, perhitungan jarak menggunakan Euclidean Distance, pengelompokan data ke cluster terdekat, hingga pembaruan centroid secara iteratif sampai kondisi konvergen tercapai. Hasil penelitian menunjukkan K-Means berhasil mengelompokkan data ke dalam tiga cluster: Cluster 1 dengan harga tinggi dan rating tinggi, Cluster 2 dengan harga sedang dan rating tertinggi, serta Cluster 3 dengan harga rendah dan rating sangat tinggi. Penelitian ini menghasilkan sistem berbasis web yang mengolah data secara otomatis dan menampilkan hasil clustering dalam tampilan mudah dipahami. Kata kunci: K-Means, Clustering, Segmentasi, GoFood, Data Mining
KONSEPTUALISASI EKONOMI PADA DEBAT CALON WAKIL PRESIDEN 2024: KAJIAN LINGUISTIK KOGNITIF Nurul Hanna Fauziyyah; Sofia Sofia
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5741

Abstract

Abstract: This study aims to identify and analyze the types of conceptual metaphors used by the vice-presidential candidates in the 2024 debate to interpret the concept of the economy in constructing linguistic reality. The research employs a descriptive qualitative approach with data consisting of metaphorical expressions found in the 2024 vice-presidential debate. The data were collected using the observation method, specifically the non-participatory observation and note-taking techniques, and were analyzed using the referential identity (matching) method. The results reveal four main conceptual metaphors underlying the economic discourse, namely: (1) THE ECONOMY IS A PLANT/ORGANISM, (2) THE ECONOMY IS A MECHANICAL MACHINE, (3) THE ECONOMY IS A LIQUID IN A CONTAINER, and (4) THE ECONOMY IS A PRISONER. These findings demonstrate that conceptual metaphors not only represent the cognitive ways of thinking of the speakers but also function as rhetorical tools used to shape public perception. Keywords: conceptual metaphor, economy, vice-presidential debate, cognitive linguistics Abstrak: Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis jenis-jenis metafora konseptual yang dgunakan para calon wakil presiden dalam debat untuk memaknai konsep ekonomi dalam mewujudkan realitas bahasa. Penelitian ini menggunakan pendekatan kualitatif deskriptis dengan data berupa ungkapan metaforis yang terdapat dalam debat calon wakil presiden tahun 2024. Data dikumpulkan menggunakan metode simak dan teknik simak libat cakap dan teknik catat, kemudian dianalisis menggunakan metode padan. Hasilnya, terdapat empat metafora konseptual yang mendasari wacana ekonomi tersebut, yaitu: 1) EKONOMI ADALAH TUMBUHAN/ORGANISME. 2) EKONOMI ADALAH MESIN MEKANIK, 3) EKONOMI ADALAH CAIRAN DALAM WADAH, dan 4) EKONOMI ADALAH NARAPIDANA. Temuan ini membuktikan bahwa metafora konseptual bukan hanya merepresentasikan cara berpikir secara kognitif seorang penutur, tetapi juga menjadi alata retoris yang digunakan untuk emembentuk persepsi publik. Kata Kunci: metafora konseptual, ekonomi, debat cawapres, linguistik kognitif
ANALYSIS OF THE EFFECTIVENESS OF THE EMPLOYEE WELL-BEING PROGRAM ON EMPLOYEE PERFORMANCE AT MANADO STATE POLYTECHNIC Jufrina Mandulangi; Precylia Ribka Rambing; Lis M.Yapanto
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5743

Abstract

Abstract: Background: The demands of the workplace in higher education institutions, including polytechnics, require optimal performance from their human resources. Stress, burnout, and work-life imbalance can reduce productivity. The Employee Well-Being (EWB) program is designed to improve the holistic well-being (physical, mental, social, and financial) of employees. However, the program's effectiveness in boosting performance in the context of Manado State Polytechnic requires empirical study. Objective: This study aims to analyze the relationship and influence of employee perceptions on the effectiveness of the Employee Well-Being Program on their performance.Method: The study used a quantitative approach with a survey method. A sample of 50 employees (lecturers and educational staff) at Manado State Polytechnic was selected using a purposive sampling technique. Data were collected through a closed questionnaire measuring two variables: (1) Perception of the Effectiveness of the EWB Program (24 indicators including physical health, mental support, social relationships, and financial well-being) and (2) Employee Performance (10 task-based and contextual indicators). Data were analyzed statistically using the Pearson Product Moment correlation test and simple linear regression analysis.Results: The analysis results show a correlation coefficient (r) of 0.712, which indicates a strong positive relationship between the perception of the effectiveness of the EWB Program and employee performance. The R Square value (coefficient of determination) is 0.507, meaning that approximately 50.7% of the variation in employee performance can be explained by variations in their perceptions of the effectiveness of the EWB Program. The results of the regression test show the equation Y = 15.245 + 0.789X, with a significance value (p)
ANALISIS SENTIMEN ULASAN APLIKASI GETCONTACT DI GOOGLE PLAY STORE MENGGUNAKAN TEKNIK NAÏVE BAYES UNTUK PENINGKATAN KUALITAS LAYANAN Mhd Tantowi Maulana; Helmi Fauzi Siregar
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5744

Abstract

Abstract: The rapid development of information technology has led to the emergence of various digital applications to meet communication needs, one of which is Getcontact. This application enables users to identify phone numbers, detect spam calls, and protect user privacy. As the number of users increases, Getcontact has received numerous reviews on the Google Play Store, containing both positive and negative opinions. These reviews are valuable for developers to evaluate service quality and user satisfaction. However, the large volume of reviews makes manual analysis inefficient. This study aims to analyze user review sentiments of the Getcontact application on the Google Play Store using the Naive Bayes Classifier algorithm. The data were collected from user reviews and processed through several stages, including case folding, tokenizing, stopword removal, and stemming. After preprocessing, the text data were transformed into numerical form using the TF-IDF method, then classified into positive and negative sentiment categories using the Naive Bayes algorithm. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes algorithm can classify user review sentiments with a high level of accuracy, making it effective for analyzing public opinions about the Getcontact application. Based on the analysis, most user reviews indicate positive sentiments, suggesting that users are generally satisfied with the application's features and services. These findings can serve as input for developers to maintain existing strengths and improve aspects that still receive user complaints. Keyword: sentiment analysis; naïve bayes; getcontact; google play store; text mining Abstrak: Perkembangan teknologi informasi yang pesat mendorong munculnya berbagai aplikasi digital untuk memenuhi kebutuhan komunikasi masyarakat, salah satunya adalah aplikasi Getcontact. Aplikasi ini memungkinkan pengguna untuk mengidentifikasi nomor telepon, mendeteksi panggilan spam, serta melindungi privasi pengguna. Seiring meningkatnya jumlah pengguna, aplikasi Getcontact memperoleh banyak ulasan di Google Play Store yang berisi opini positif maupun negatif. Ulasan tersebut dapat menjadi sumber informasi penting bagi pengembang untuk menilai kualitas layanan dan tingkat kepuasan pengguna. Namun, jumlah ulasan yang sangat besar membuat analisis manual menjadi tidak efektif. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Getcontact di Google Play Store dengan menggunakan algoritma Naive Bayes Classifier. Data penelitian diperoleh melalui pengumpulan ulasan pengguna yang kemudian diproses melalui beberapa tahapan, yaitu case folding, tokenizing, stopword removal, dan stemming. Setelah dilakukan preprocessing, data diubah menjadi bentuk numerik menggunakan metode TF-IDF, lalu diklasifikasikan ke dalam kategori sentimen positif dan negatif menggunakan algoritma Naive Bayes. Evaluasi model dilakukan dengan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu mengklasifikasikan sentimen ulasan pengguna dengan tingkat akurasi yang tinggi, sehingga efektif digunakan untuk menganalisis opini publik terhadap aplikasi Getcontact. Berdasarkan hasil analisis, mayoritas ulasan pengguna menunjukkan sentimen positif yang mengindikasikan bahwa pengguna merasa puas terhadap fitur dan layanan aplikasi. Hasil ini dapat menjadi masukan bagi pengembang untuk mempertahankan keunggulan yang ada serta memperbaiki aspek-aspek yang masih mendapat keluhan dari pengguna. Kata kunci: analisis sentimen; naïve bayes; getcontact; google play store; text mining
KLASIFIKASI TIPE KACA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Muhammad Azwar Al Ayyub; Weny Nur Afdilla Simangunsong; Dini Farhatun; Emi Dea; Selfina Agustin; Zulfa Ar Rahman; Muhammad Ridho
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5745

Abstract

Abstract: Glass is a material that is widely used in various fields, such as construction, the automotive industry, and household appliances. Each type of glass has different characteristics based on its chemical composition and production process. Problems arise when the process of identifying glass types is still done manually, which is time-consuming, costly, and prone to error. This study aims to apply the K-Nearest Neighbor (K-NN) method in classifying glass types based on their chemical content attributes. The data in this study was sourced from Kaggle, namely the Glass Identification Dataset. The data used consisted of several chemical features, such as Na, Mg, Al, Si, K, Ca, Ba, and Fe, with seven categories of glass classes. The results showed that the K-NN method was able to classify glass types well and could be an effective solution to assist in the automatic glass identification process. Keyword: Classification, K-Nearest Neighbor, Data Mining, Types of Glass. Abstrak: Kaca merupakan material yang banyak digunakan dalam berbagai bidang, seperti konstruksi, industri otomotif, dan peralatan rumah tangga. Setiap jenis kaca memiliki karakteristik yang berbeda berdasarkan komposisi kimia dan proses produksinya. Permasalahan muncul ketika proses identifikasi jenis kaca masih dilakukan secara manual, sehingga membutuhkan waktu, biaya, dan berpotensi menimbulkan kesalahan. Penelitian ini bertujuan untuk menerapkan metode K-Nearest Neighbor (K-NN) dalam mengklasifikasikan jenis kaca berdasarkan atribut kandungan kimianya. Data dalam penelitian ini bersumber dari Kaggle, yaitu Glass Identification Dataset. Data yang digunakan terdiri dari beberapa fitur kimia, seperti Na, Mg, Al, Si, K, Ca, Ba, dan Fe, dengan tujuh kategori kelas kaca. Hasil penelitian menunjukkan bahwa metode KNN mampu mengklasifikasikan jenis kaca dengan baik dan dapat menjadi solusi yang efektif untuk membantu proses identifikasi kaca secara otomatis. Kata kunci: Klasifikasi, K-Nearest Neighbor, Data Mining, Jenis Kaca.
ANALISIS KUALITAS PRODUKSI AYAM BROILER MENGGUNAKAN METODE K-MEANS CLUSTERING DAN ALGORITMA C4.5 Oriza Rama Saputra; Rini Sovia; Musli Yanto
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5747

Abstract

Abstract: Broiler chicken production is influenced by various factors, such as feed, environment, and maintenance management, which generate large amounts of complex production data. This condition causes the assessment and decision-making processes related to production quality to often be suboptimal and not based on in-depth data analysis. This study aims to implement the K-Means method and C4.5 algorithm to produce an analysis process that can be used as a solution in determining broiler chicken production quality in the South Coast region. The K-Means method is used to classify broiler chicken production data based on similar characteristics to facilitate pattern identification. The C4.5 algorithm was used to build a decision tree model to determine and predict broiler chicken production quality based on the most influential attributes. The research dataset was sourced from farm data in the South Coast region, with a total of 157 data points obtained. The clustering results presented three main segments, namely 94 with good results, 58 data with moderate results, and 5 data with poor results. Meanwhile, the C4.5 algorithm was built based on the clustering results from K-Means. The accuracy was calculated using the F1 score, with an accuracy of 93,75%. Keywords: Broiler Chicken;K-Means; C4.5 Algorithm. Abstrak: Produksi ayam broiler dipengaruhi oleh berbagai faktor, seperti pakan, lingkungan, dan manajemen pemeliharaan, yang menghasilkan data produksi dalam jumlah besar dan bersifat kompleks. Kondisi ini menyebabkan proses penilaian dan pengambilan keputusan terkait kualitas produksi sering kali belum optimal dan kurang didasarkan pada analisis data yang mendalam. Penelitian dilakukan bertujuan untuk Mengimplementasikan metode K-Means dan algoritma C4.5 untuk menghasilkan proses analisis yang dijadikan solusi dalam penentuan kualitas produksi ayam broiler di wiliyah Pesisir Selatan. Metode K-Means digunakan untuk mengklasifikasikan data produksi ayam broiler berdasarkan kesamaan karakteristik untuk memudahkan identifikasi pola. Algoritma C4.5 Digunakan untuk membangun model pohon keputusan dalam menentukan dan memprediksi kualitas produksi ayam broiler berdasarkan atribut yang paling berpengaruh. Dataset penelitian bersumber dari data peternakan di wilayah Pesisir Selatan, Dengan total data ada 157 data yang didapatkan. Hasil klustering menyajikan tiga segmen utama yaitu 94 dengan hasil baik, 58 data dengan hasil sedang dan 5 data dengan hasil buruk, Sedangkan Algoritma C4.5 dibangun berdasarkan hasil klustering dari K-Means. Perhitungan Hasil akurasi dengan f1 score Dengan hasil akurasi 93,75%. Kata Kunci: Ayam Broiler, K-Means, Algoritma C4.5
TINGKAT KEBERHASILAN UMKM DESA BERDASARKAN MODAL DAN STRATEGI PEMASARAN DENGAN NAÏVE BAYES Putra Bagus Utama; Dicky Apdillah
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5749

Abstract

Abstract: This study aims to classify the success level of village MSMEs based on capital and marketing strategies using the Naive Bayes algorithm. Micro, Small, and Medium Enterprises often face challenges such as limited capital and ineffective marketing strategies, which may hinder business performance. In this research, MSME data were collected through questionnaires covering variables such as initial capital, capital source, type of marketing strategy, promotional media, and business duration. The data were then processed and analyzed using the Naive Bayes method to predict MSME success levels categorized as high, medium, and low. The results indicate that the Naive Bayes model provides good prediction accuracy and is able to identify the most influential variables affecting MSME success. This study is expected to serve as a reference for MSME practitioners and local governments in designing more effective and targeted business development strategies. Keywords: MSMEs, capital, marketing strategy, classification, Naive Bayes Abstrak: Penelitian ini bertujuan untuk mengklasifikasikan tingkat keberhasilan UMKM desa berdasarkan modal dan strategi pemasaran menggunakan algoritma Naive Bayes. Permasalahan utama yang sering dihadapi UMKM adalah keterbatasan modal serta pemilihan strategi pemasaran yang kurang efektif, sehingga berdampak pada rendahnya tingkat keberhasilan usaha. Dalam penelitian ini, data UMKM dikumpulkan melalui kuesioner yang mencakup variabel modal awal, sumber modal, bentuk strategi pemasaran, media promosi, dan lama usaha. Data tersebut kemudian diolah dan dianalisis menggunakan metode Naive Bayes untuk memprediksi tingkat keberhasilan UMKM yang dikategorikan menjadi tinggi, sedang, dan rendah. Hasil penelitian menunjukkan bahwa model Naive Bayes mampu memberikan akurasi prediksi yang baik serta mengidentifikasi variabel yang paling berpengaruh terhadap keberhasilan UMKM. Penelitian ini diharapkan dapat menjadi acuan bagi pelaku UMKM maupun pemerintah desa dalam merancang strategi pengembangan usaha yang lebih efektif dan tepat sasaran. Kata Kunci : UMKM, modal, strategi pemasaran, klasifikasi, Naive Bayes
IMPLEMENTASI METODE WEIGHTED PRODUCT UNTUK PENILAIAN PESERTA PELATIHAN TENAGA KERJA BERBASIS KOMPETENSI PADA DISNAKER KABUPATEN BENGKULU TENGAH Adjie Danang Aprianto; Devi Sartika; Dimas Aulia Trianggana
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5750

Abstract

Abstract: The assessment of competency-based labor training participants is an important aspect in ensuring the quality and effectiveness of training programs organized by DISNAKER of Central Bengkulu Regency. However, the manual assessment process is often subjective and time-consuming. To overcome this problem, this study implemented Weighted Product (WP) method as a decision support method in the training participant assessment process. WP method was chosen for its ability to handle various assessment criteria by assigning weights according to their level of importance. This study involved several assessment criteria, such as attendance, theoretical evaluation results, practical evaluation results, and attitude during training. The results of the implementation show that WP method can provide accurate and consistent rankings and assist DISNAKER in determining the best participants objectively. Thus, this WP-based assessment system is expected to improve transparency and efficiency in the workforce training evaluation process at DISNAKER of Central Bengkulu Regency. Keywords: Weighted Product (WP), Decision Support System, Participant Assessment, Workforce Training, DISNAKER. Abstrak: Penilaian peserta pelatihan tenaga kerja berbasis kompetensi merupakan aspek penting dalam memastikan kualitas dan efektivitas program pelatihan yang diselenggarakan oleh Dinas Tenaga Kerja (Disnaker) Kabupaten Bengkulu Tengah. Namun, proses penilaian yang dilakukan secara manual seringkali kurang objektif dan memerlukan waktu yang cukup lama. Untuk mengatasi permasalahan tersebut, penelitian ini mengimplementasikan metode Weighted Product (WP) sebagai metode pendukung keputusan dalam proses penilaian peserta pelatihan. Metode WP dipilih karena kemampuannya dalam menangani berbagai kriteria penilaian dengan memberikan bobot sesuai tingkat kepentingannya. Penelitian ini melibatkan beberapa kriteria penilaian, seperti kehadiran, hasil evaluasi teori, hasil evaluasi praktik, dan sikap selama pelatihan. Hasil implementasi menunjukkan bahwa metode WP dapat memberikan hasil peringkat yang akurat, konsisten, dan membantu pihak Disnaker dalam menentukan peserta terbaik secara objektif. Dengan demikian, sistem penilaian berbasis WP ini diharapkan dapat meningkatkan transparansi dan efisiensi dalam proses evaluasi pelatihan tenaga kerja di lingkungan Disnaker Kabupaten Bengkulu Tengah. Kata Kunci: Weighted Product(WP), Sistem Pendukung Keputusan, Penilaian Peserta, Pelatihan Tenaga Kerja, Disnaker
KLASIFIKASI IRIS SPECIES MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN) Zunaida Sitorus; Nurliana Nurliana; Septinur Selase; Desi Patmala; Sulhani Nuraini; Yusria Aritia; Izwal Jamil Margolang
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5751

Abstract

Abstract : Iris species classification is an important topic in the field of data mining and machine learning because it is commonly used as a benchmark dataset for classification methods. This study aims to design and implement an information system that can classify Iris flower species using the K-Nearest Neighbor (KNN) method. The dataset used in this research is the Iris dataset, which consists of 150 data records with four attributes: sepal length, sepal width, petal length, and petal width, and three classes, namely Iris Setosa, Iris Versicolor, and Iris Virginica. The KNN method works by calculating the distance between test data and training data using the Euclidean distance formula and determining the class based on the majority of the nearest neighbors. The results of the study show that the KNN method is able to classify Iris species accurately with a good level of performance. Based on the testing results, the developed system can assist users in identifying Iris species effectively and efficiently. In conclusion, the K-Nearest Neighbor method can be successfully applied in an information system for Iris species classification. Keywords: Classification, Iris Dataset, K-Nearest Neighbor, Data Mining, Machine Learning Abstrak : Klasifikasi spesies Iris merupakan topik penting dalam bidang data mining dan machine learning karena sering digunakan sebagai dataset standar dalam pengujian metode klasifikasi. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi yang dapat mengklasifikasikan spesies bunga Iris menggunakan metode K-Nearest Neighbor (KNN). Dataset yang digunakan adalah dataset Iris yang terdiri dari 150 data dengan empat atribut, yaitu panjang sepal, lebar sepal, panjang petal, dan lebar petal, serta tiga kelas yaitu Iris Setosa, Iris Versicolor, dan Iris Virginica. Metode KNN bekerja dengan menghitung jarak antara data uji dan data latih menggunakan rumus Euclidean Distance, kemudian menentukan kelas berdasarkan mayoritas tetangga terdekat. Hasil penelitian menunjukkan bahwa metode KNN mampu mengklasifikasikan spesies Iris dengan tingkat akurasi yang baik. Berdasarkan hasil pengujian, sistem yang dikembangkan dapat membantu pengguna dalam mengidentifikasi spesies Iris secara efektif dan efisien. Dengan demikian, metode K-Nearest Neighbor dapat diterapkan dengan baik dalam sistem informasi klasifikasi Iris Species. Kata Kunci : Klasifikasi, Dataset Iris, K-Nearest Neighbor, Data Mining, Machine Learning
IMPLEMENTASI PENDIDIKAN KARAKTER PADA MATA PELAJARAN SEJARAH DI SMA BUDI OETOMO PONTIANAK Miftahul Jannah; Pujo Sukino
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5752

Abstract

Abstract: This study aims to describe in a complete and comprehensive manner the implementation of character education in the history subject of interest at SMA Boedi Oetomo Pontianak. The research method used is descriptive with a qualitative approach. The research techniques used are direct communication techniques, indirect communication and documentation. The research tools are observation guides, interview guides and learning documents. The results of this study include: 1) learning planning that includes students' character values, especially in history learning by teachers has been carried out well and according to expectations can be seen from the preparation of teaching modules that are in accordance with the applicable curriculum and teachers who always refer to the teaching modules made. 2) History learning is one of the alternatives used by SMA Budi Oetomo Pontianak teachers to instill character values in students, history subjects provide students with an overview of having a character attitude. The character values instilled in students are: 1) Religious, 2) Discipline, 3) Tolerance, 4) Nationalism, 5) Love of the Motherland, 6) Responsibility. 7) Caring for Others. 8) Not Destroying Nature. 9) Independence. 3) Factors hindering teachers from instilling character values in students can be seen in the students' sometimes inability to connect when asked, perhaps due to a small number or some students being somewhat slow thinkers. However, generally speaking, teachers do not face many obstacles. This is due to the small number of students, the fact that most of the students live with other people, making them easy to manage, and their good character. Keywords: Character Education, History Lessons Abstrak: Penelitian ini bertujuan untuk mendeskripsikan secara utuh dan menyeluruh tentang implementasi pendidikan karakter pada mata pelajaran sejarah peminatan di SMA Boedi Oetomo Pontianak. Metode penelitian yang digunakan deskriptif dengan pendekatan kualitatif. teknik penelitian yang digunakan teknik komunikasi langsung, komunikasi tidak langsung dan dokumentasi. Alat penelitian panduan observasi, panduan wawancara dan dokumen pembelajaran. Hasil penelitian ini antara lain: 1) perencanaan pembelajaran yang memuat nilai-nilai karakter siswa khususnya pada pembelajaran sejarah oleh guru sudah dilakukan perencanaan yang baik dan sesuai harapan dapat dilihat dari penyusunan modul ajar yang seseuai dengan kurikulum yang berlaku serta guru yang selalu berpedoman pada modul ajar yang dibuat. 2) Pembelajaran sejarah merupakan salah satu alternatif yang digunakan guru SMA Budi Oetomo Pontianak untuk menanamkan nilai-nilai karakter kepada peserta didik, mata pelajaran sejarah memberikan siswa gambaran untuk memiliki sikap yang berkarakter. Nilai- nilai karakter yang ditanamkan pada siswa yaitu : 1).Religius, 2).Disiplin, 3).Toleransi, 4).Semangat Nasionalisme, 5).Cinta Tanah Air, 6).Tanggung Jawab. 7) Peduli Sesama. 8).Tidak Merusak Alam. 9).Mandiri. 3) faktor penghambat guru dalam menanamkan nilai-nilai karakter siswa dapat dilihat dari siswa-siswa kadang kurang nyambung jika disuruh, karena ada yang mungkin sebagian kecil atau beberapa siswa yang agak lambat dalam berfikir. Namun secara garis besar tidak banyak kendala yang dihadapi guru, hal ini karena jumlah siswa yang sedikit, siswa yang rata-rata tinggal dengan orang lain sehingga mudah di atur siswanya dan mereka juga memiliki karakter yang baik Kata Kunci : Pendidikan Karakter Pelajaran Sejarah

Page 294 of 365 | Total Record : 3645

 292   293   294   295   296 

Filter by Year

2018 2026


Reset
Filter By Issues
All Issue Vol. 9 No. 4 (2026): August 2026 (1) Vol. 9 No. 3 (2026): June 2026 Vol. 9 No. 2 (2026): April 2026 Vol 9, No 1 (2026): February 2026 Vol. 9 No. 1 (2026): February 2026 Vol. 8 No. 4 (2025): November 2025 Vol 8, No 4 (2025): November 2025 Vol. 8 No. 3 (2025): August 2025 Vol 8, No 3 (2025): August 2025 Vol 8, No 2 (2025): May 2025 Vol. 8 No. 2 (2025): May 2025 Vol. 8 No. 1 (2025): February 2025 Vol 8, No 1 (2025): February 2025 Vol. 7 No. 4 (2024): November 2024 Vol 7, No 4 (2024): November 2024 Vol 7, No 3 (2024): August 2024 Vol. 7 No. 3 (2024): August 2024 Vol 7, No 2 (2024): May 2024 Vol. 7 No. 2 (2024): May 2024 Vol 7, No 1 (2024): February 2024 Vol. 7 No. 1 (2024): February 2024 Vol 6, No 3 (2023): October 2023 Vol. 6 No. 3 (2023): October 2023 Vol 6, No 2 (2023): June 2023 Vol 6, No 1 (2023): February 2023 Vol 5, No 3 (2022): October 2022 Vol. 5 No. 3 (2022): October 2022 Vol 5, No 2 (2022): June 2022 Vol 5, No 1 (2022): February 2022 Vol 4, No 3 (2021): October 2021 Vol 4, No 2 (2021): June 2021 Vol 4, No 1 (2021): February 2021 Vol 3, No 2 (2020): August 2020 Vol 3, No 1 (2020): February 2020 Vol 2, No 2 (2019): August 2019 Vol 2, No 1 (2019): February 2019 Vol 1, No 2 (2018): August 2018 Vol 1, No 2 (2018): August 2018 Vol 1, No 1 (2018): February 2018 Vol 1, No 1 (2018): February 2018 More Issue