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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.
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Articles 3,645 Documents
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ANALISIS PENANGANAN DATA TIDAK SEIMBANG TERHADAP KINERJA KLASIFIKASI SENTIMEN MULTIKELAS PADA ULASAN MARKETPLACE TOKOPEDIA Nauval Alfarizi; Satria Sinurat; Adi Putra; Muhammad Amin; Prima Lydia
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.v9i1.5804

Abstract

Abstract: The development of digital marketplaces has led to an increasing number of user reviews, which can be used to understand consumer perceptions of products and services. However, sentiment analysis in marketplace reviews faces a major challenge: class imbalance, where positive sentiment often dominates to an extreme. This study aims to analyze the effects of various imbalanced data-handling techniques on the performance of machine-learning-based multiclass sentiment classification in Tokopedia marketplace reviews. The dataset used consists of 56,981 reviews with three sentiment classes, with more than 97% of them being positive. Feature extraction was performed using the TF-IDF method, resulting in 17,765 features. The handling of data imbalance was tested through four scenarios: class weighting, Random Oversampling, SMOTE, and ADASYN, with the Naive Bayes, Logistic Regression, and Random Forest algorithms. The experimental results show that Random Forest with SMOTE achieves the highest accuracy of 0.9749 but has limitations in recognizing minority classes, with a recall of 0.3786. In contrast, Logistic Regression with Random Oversampling provides the most balanced performance with the highest F1-score (macro) value of 0.4992 and recall of 0.5866. Keywords: Analysis, Sentiment, Imbalanced Data, Multi-Class Classification F1-Score Abstrak: Perkembangan marketplace digital menyebabkan meningkatnya jumlah ulasan pengguna yang dapat dimanfaatkan untuk memahami persepsi konsumen terhadap produk dan layanan. Namun, analisis sentimen pada ulasan marketplace menghadapi tantangan utama berupa ketidakseimbangan distribusi kelas, di mana sentimen positif sering kali mendominasi secara ekstrem. Penelitian ini bertujuan untuk menganalisis pengaruh berbagai teknik penanganan data tidak seimbang terhadap kinerja klasifikasi sentimen multikelas pada ulasan marketplace Tokopedia berbasis machine learning. Dataset yang digunakan terdiri dari 56.981 ulasan dengan tiga kelas sentiment, di mana proporsi sentimen positif mencapai lebih dari 97%. Ekstraksi fitur dilakukan menggunakan metode TF-IDF yang menghasilkan 17.765 fitur. Penanganan ketidakseimbangan data diuji melalui empat skenario, yaitu class weighting, Random Oversampling, SMOTE, dan ADASYN, dengan algoritma Naive Bayes, Logistic Regression, dan Random Forest. Hasil eksperimen menunjukkan bahwa Random Forest dengan SMOTE menghasilkan akurasi tertinggi sebesar 0,9749, namun memiliki keterbatasan dalam mengenali kelas minoritas dengan nilai recall 0,3786. Sebaliknya, Logistic Regression dengan Random Oversampling memberikan performa paling seimbang dengan nilai F1-score (macro) tertinggi sebesar 0,4992 dan recall 0,5866. Kata kunci: Analisis, Sentimen, Data Tidak Seimbang, Klasifikasi Multi Kelas F1-Score
PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING DALAM MEMPREDIKSI TINGKAT STRES MAHASISWA BERDASARKAN FAKTOR AKADEMIK DAN NON-AKADEMIK Ahmad Jihad Al Fayed; Surya Darma; Muhammad Hizbul Aqsha; Surya Maruli P Pardede; Muhammad Amin
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.v9i1.5805

Abstract

Abstract: Stress among students is a growing phenomenon due to high academic demands, changes in the social environment, and various non-academic pressures faced during their studies. Stress that is not managed properly can have a negative impact on students' mental health, motivation to study, and academic achievement. Therefore, an approach is needed that can identify and predict students' stress levels objectively and based on data. This study aims to analyze and compare the performance of several machine learning algorithms in predicting student stress levels based on academic and non-academic factors. The dataset used in this study is Student Stress Factors, which includes various variables such as Sleep Quality, Academic Achievement, Study Load, Frequency of Headaches, Extracurricular Activities, Level of Social Support, Screen Time, etc. The algorithms applied are Support Vector Machine and Naive Bayes. This research is expected to contribute to the development of a decision support system for early detection of student stress levels, as well as serve as a reference for educational institutions in designing strategies for the prevention and management of mental health issues in higher education environments. Keywords: Machine Learning, Support Vector Machine, Naive Bayes, Stress, Student Abstrak: Stres pada mahasiswa merupakan fenomena yang semakin meningkat seiring dengan tuntutan akademik yang tinggi, perubahan lingkungan sosial, serta berbagai tekanan non-akademik yang dihadapi selama masa studi. Kondisi stres yang tidak dikelola dengan baik dapat berdampak negatif terhadap kesehatan mental, motivasi belajar, serta capaian akademik mahasiswa. Oleh karena itu, diperlukan suatu pendekatan yang mampu mengidentifikasi dan memprediksi tingkat stres mahasiswa secara objektif dan berbasis data. Penelitian ini bertujuan untuk menganalisis serta membandingkan kinerja algoritma machine learning dalam memprediksi tingkat stres mahasiswa berdasarkan faktor akademik dan non-akademik. Dataset yang digunakan pada penelitian ini adalah Student Stress Factors, yang mencakup berbagai variabel seperti Kualitas Tidur, Prestasi Akademik, Beban Studi, Frekuensi Sakit Kepala, Kegiatan Ekstrakurikuler, Tingkat Dukungan Sosial, Jam Waktu Layar, dll. Algoritma yang diterapkan yaitu Support Vector Machine dan Naive Bayes dengan akurasi tertinggi dihasilkan oleh Algoritma SVM dengan akurasi 85% sedangkan NV memiliki akurasi 83%. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem pendukung keputusan untuk deteksi dini tingkat stres mahasiswa, serta menjadi referensi bagi institusi pendidikan dalam merancang strategi pencegahan dan penanganan masalah kesehatan mental di lingkungan perguruan tinggi. Kata kunci: Machine Learning, Support Vector Machine, Naive Bayes, Stres, Mahasiswa
IMPLEMENTATION OF THE INTERNET OF THINGS IN CREATING SMART CLASSROOMS Subhan Hafiz Nanda Ginting; Dewi Wahyuni; Nurmala Sridewi; M. Rhifky Wayahdi; Surya Darma
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.v9i1.5806

Abstract

Abstract: The development of digital technology has driven the transformation of educational services through the implementation of data-based learning systems and smart devices. One of the emerging approaches is the use of the Internet of Things (IoT) in building smart classrooms, which are classrooms capable of integrating physical devices, sensors, and information systems to improve learning efficiency and learning environment management. This study aims to analyze the implementation of IoT in the formation of smart classrooms, covering aspects of system design, device integration, and evaluation of its effectiveness in supporting the teaching and learning process. The research method used is a quantitative and experimental approach, by designing an IoT-based smart classroom prototype that integrates temperature, humidity, light intensity, and presence detection sensors, as well as device control such as lights, air conditioning, and projectors through an automatic system and remote control. Data was collected through device performance measurements, network stability tests, and questionnaires distributed to users (teachers and students) to assess the system's ease of use and usefulness. The results showed that the implementation of IoT in classrooms can improve the efficiency of facility management through device automation, facilitate real-time monitoring of classroom conditions, and provide a more comfortable and responsive learning environment. In addition, the developed system demonstrated stable data communication performance with low latency within acceptable operational limits. These findings indicate that the application of IoT in smart classrooms has the potential to contribute significantly to improving the quality of learning and classroom management, particularly in supporting a technology-based education ecosystem. This study recommends further development in the areas of data security, device interoperability, and integration with Learning Management Systems (LMS) to strengthen the sustainable implementation of smart classrooms. Keywords: Internet of Things, Smart Classroom, Classroom Automation, Sensors, Technology Based Education. Abstrak: Perkembangan teknologi digital mendorong transformasi layanan pendidikan melalui penerapan sistem pembelajaran berbasis data dan perangkat cerdas. Salah satu pendekatan yang berkembang adalah pemanfaatan Internet of Things (IoT) dalam membangun smart classroom, yaitu ruang kelas yang mampu mengintegrasikan perangkat fisik, sensor, serta sistem informasi untuk meningkatkan efisiensi pembelajaran dan pengelolaan lingkungan belajar. Penelitian ini bertujuan untuk menganalisis implementasi IoT dalam pembentukan smart classroom, mencakup aspek desain sistem, integrasi perangkat, serta evaluasi efektivitasnya dalam mendukung proses belajar mengajar. Metode penelitian yang digunakan adalah pendekatan kuantitatif dan eksperimental, dengan merancang prototipe smart classroom berbasis IoT yang mengintegrasikan sensor suhu, kelembapan, intensitas cahaya, deteksi kehadiran, serta pengendalian perangkat seperti lampu, pendingin ruangan, dan proyektor melalui sistem otomatis maupun kendali jarak jauh. Data dikumpulkan melalui pengukuran kinerja perangkat, uji stabilitas jaringan, serta penyebaran kuesioner kepada pengguna (guru dan siswa) untuk menilai tingkat kemudahan penggunaan dan kebermanfaatan sistem. Hasil penelitian menunjukkan bahwa implementasi IoT pada ruang kelas mampu meningkatkan efisiensi pengelolaan fasilitas melalui otomasi perangkat, mempermudah monitoring kondisi kelas secara real-time, serta memberikan dukungan lingkungan belajar yang lebih nyaman dan responsif. Selain itu, sistem yang dikembangkan menunjukkan performa komunikasi data yang stabil dengan tingkat keterlambatan (latency) yang rendah dalam batas operasional yang dapat diterima. Temuan ini mengindikasikan bahwa penerapan IoT dalam smart classroom berpotensi memberikan kontribusi signifikan terhadap peningkatan kualitas pembelajaran dan manajemen kelas, khususnya dalam mendukung ekosistem pendidikan berbasis teknologi. Penelitian ini merekomendasikan pengembangan lanjutan pada aspek keamanan data, interoperabilitas perangkat, serta integrasi dengan Learning Management System (LMS) untuk memperkuat implementasi smart classroom secara berkelanjutan. Kata Kunci: Internet Of Things, Smart Classroom, Otomasi Ruang Kelas, Sensor, Pendidikan Berbasis Teknologi.
PREDIKSI PENJUALAN SUPERMARKET MENGGUNAKAN JARINGAN SYARAF TIRUAN LONG SHORT-TERM MEMORY (LSTM) Lima Hartimar Rambe; Yuke Manza; Kiki Putri Ani Siregar; Roslina Roslina
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.v9i1.5807

Abstract

Abstract: Sales forecasting is a crucial aspect of supermarket operations, as it supports inventory management, production planning, and strategic decision-making. Sales data typically exhibit complex patterns such as trends, seasonality, and fluctuations, requiring modeling methods capable of handling nonlinear time-series characteristics. This study employs the Long Short-Term Memory (LSTM) model, an advanced form of Recurrent Neural Network (RNN) designed to capture long-term dependencies and overcome the vanishing gradient problem. The secondary dataset was obtained from the Kaggle platform, consisting of 20 features and a total of 1,000 records. The LSTM model was constructed using 50 neurons in the LSTM layer and a single dense output layer. Training was conducted for 100 epochs using the Adam optimizer and Mean Squared Error (MSE) as the loss function. The training process showed a consistent decrease in loss, reaching approximately 0.0193, while evaluation using Root Mean Squared Error (RMSE) indicated that the model effectively learned historical patterns. Visualization of predictions on the test dataset demonstrated that the model successfully followed sales trends, although it was less responsive to extreme fluctuations. Overall, the LSTM model proved effective for daily sales forecasting and can serve as a valuable tool for operational planning in supermarkets. Keywords: LSTM, Sales Forecasting, Time Series, Deep Learning, RMSE, Supermarket. Abstrak: Peramalan penjualan merupakan aspek penting dalam operasional supermarket karena berperan besar dalam pengelolaan inventaris, perencanaan produksi, serta pengambilan keputusan strategis. Data penjualan umumnya memiliki pola tren, musiman, dan fluktuasi yang kompleks sehingga memerlukan metode pemodelan yang mampu menangani karakteristik deret waktu nonlinear. Penelitian ini menggunakan model Long Short-Term Memory (LSTM), sebuah pengembangan Recurrent Neural Network (RNN) yang efektif dalam menangkap dependensi jangka panjang dan mengatasi masalah vanishing gradient. Data sekunder diperoleh dari platform Kaggle dengan 20 fitur dan total 1.000 record. Model LSTM dibangun menggunakan 50 unit neuron pada lapisan LSTM dan satu lapisan dense sebagai output. Model dilatih selama 100 epoch menggunakan optimizer Adam dan fungsi loss MSE. Hasil pelatihan menunjukkan penurunan loss yang stabil hingga mencapai nilai sekitar 0,0193, sedangkan evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan bahwa model mampu mempelajari pola historis dengan baik. Visualisasi prediksi pada data pengujian memperlihatkan bahwa model mampu mengikuti tren pergerakan penjualan meskipun masih kurang responsif terhadap fluktuasi ekstrem. Secara keseluruhan, model LSTM terbukti efektif dalam memprediksi penjualan harian dan dapat digunakan sebagai dasar pengambilan keputusan dalam perencanaan operasional supermarket. Kata kunci: LSTM, Peramalan Penjualan, Deret Waktu, Deep Learning, RMSE, Supermarket.
AKIBAT HUKUM TERHADAP PRODUK MAKANAN DAN MINUMAN TIDAK BESERTIFIKAT HALAL DALAM PERSPEKTIF UNDANG-UNDANG NOMOR 33 TAHUN 2014 TENTANG JAMINAN PRODUK HALAL Sofian Sofian; Malika Aulia
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.v9i1.5808

Abstract

Abstract: Halal products are a fundamental need for Muslims as a form of adherence to religious teachings. The Indonesian government, through Law Number 33 of 2014 concerning Halal Product Assurance, requires all food and beverage products distributed in Indonesia to be halal certified. However, in practice, many businesses, particularly Micro and Small Enterprises (MSEs), have not complied with this obligation. This study aims to analyze the legal consequences for businesses that lack halal certification and identify the obstacles faced in the process of obtaining such certification. This study uses a normative juridical method with a statutory regulatory approach, supported by interviews as empirical data. The results indicate that businesses that lack halal certification are subject to administrative sanctions as stipulated in Government Regulation Number 42 of 2024, which include written warnings, administrative fines, and product withdrawals. Obstacles identified include low understanding among businesses, lack of government outreach, and limited access to certification facilities. Therefore, strengthening regulatory implementation and increasing legal awareness among businesses is necessary to ensure maximum protection for Muslim consumers. Keywords: Legal Consequences, Halal Certificate, Food and Beverage Products, BPJPH, Law No. 33 of 2014 Abstrak: Produk halal merupakan kebutuhan fundamental bagi umat Islam sebagai bentuk kepatuhan terhadap ajaran agama. Pemerintah Indonesia melalui Undang-Undang Nomor 33 Tahun 2014 tentang Jaminan Produk Halal mewajibkan seluruh produk makanan dan minuman yang beredar di Indonesia untuk bersertifikat halal. Namun, dalam praktiknya, masih banyak pelaku usaha, terutama pelaku Usaha Mikro dan Kecil (UMK), yang belum melaksanakan kewajiban tersebut. Penelitian ini bertujuan untuk menganalisis akibat hukum yang timbul terhadap pelaku usaha yang tidak memiliki sertifikat halal serta mengidentifikasi kendala yang dihadapi dalam proses perolehan sertifikat tersebut. Penelitian ini menggunakan metode yuridis normatif dengan pendekatan peraturan perundang-undangan dan didukung oleh wawancara sebagai data empiris. Hasil penelitian menunjukkan bahwa pelaku usaha yang tidak memiliki sertifikat halal dapat dikenai sanksi administratif sebagaimana diatur dalam Peraturan Pemerintah Nomor 42 Tahun 2024, yang mencakup peringatan tertulis, denda administratif, dan penarikan produk dari peredaran. Kendala yang ditemukan antara lain rendahnya pemahaman pelaku usaha, kurangnya sosialisasi dari pemerintah, serta keterbatasan akses terhadap fasilitas sertifikasi. Oleh karena itu, diperlukan penguatan implementasi regulasi dan peningkatan kesadaran hukum bagi pelaku usaha agar perlindungan konsumen Muslim dapat terjamin secara maksimal. Kata Kunci: Akibat Hukum, Sertifikat Halal, Produk Makanan dan Minuman, BPJPH, UU No. 33 Tahun 2014
LEVERAGING ARTIFICIAL INTELLIGENCE (AI) FOR ORGANIZATIONAL COMMUNICATION AND OPERATIONAL PERFORMANCE: THE CASE OF BANK BRI Agnita Yolanda; Neni Triastuti
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.v9i1.5812

Abstract

Abstract: This research aims to determine and analyze the role of AI in the BRI Medan North Sumatra working place. Advances in artificial intelligence (AI) technology have had a significant impact on banking sector operations, including at Bank Rakyat Indonesia (BRI). The implementation of AI at BRI plays a role in improving operational process efficiency, customer service quality, and supporting data-driven decision-making. AI technology is applied in various areas, including customer service through chatbots, creditworthiness analysis and risk management, fraud prevention and detection, and personalized banking products and services. The result indicated that AI's ability to process data quickly and accurately enables BRI to minimize operational errors, optimize costs, and increase customer satisfaction. However, the implementation of AI also faces several challenges, such as data security protection, human resource readiness, and regulatory compliance. Therefore, the use of AI at BRI needs to be accompanied by strengthened governance and improved human resource competency to maximize the benefits of this technology in a sustainable manner. Keywords: Artificial Intelligence, Organizational Communication, Operational Performance, Digital Transformation, Banking Sector . Abstrak: Penelitian ini bertujuan untuk mengetahui dan menganalisis peran AI di lingkungan kerja bank BRI Medan Sumatra Utara. Kemajuan teknologi kecerdasan buatan (Artificial Intelligence/AI) telah memberikan dampak yang signifikan terhadap operasional sektor perbankan, termasuk di Bank Rakyat Indonesia (BRI). Implementasi AI di BRI berperan dalam meningkatkan efisiensi proses operasional, mutu layanan kepada nasabah, serta mendukung pengambilan keputusan yang berbasis analisis data. Teknologi AI diterapkan pada berbagai bidang, antara lain layanan nasabah melalui chatbot, analisis kelayakan kredit dan pengelolaan risiko, pencegahan dan pendeteksian penipuan, serta personalisasi produk dan layanan perbankan. Hasil penelitian menunjukkan bahwa kemampuan AI dalam memproses data secara cepat dan akurat memungkinkan BRI untuk meminimalkan kesalahan operasional, mengoptimalkan biaya, dan meningkatkan tingkat kepuasan nasabah. Namun demikian, penerapan AI juga dihadapkan pada sejumlah tantangan, seperti perlindungan keamanan data, kesiapan sumber daya manusia, serta kepatuhan terhadap regulasi. Oleh karena itu, pemanfaatan AI di BRI perlu disertai dengan penguatan tata kelola dan peningkatan kompetensi sumber daya manusia agar manfaat teknologi ini dapat dimaksimalkan secara berkelanjutan. Kata Kunci: Kecerdasan Buatan, Komunikasi Organisasi, Kinerja Operasional, Transformasi Digital, Sektor Perbankan
PENGARUH MOTIVASI KERJA DAN BUDAYA ORGANISASI TERHADAP DISIPLIN KERJA PEGAWAI SATUAN POLISI PAMONG PRAJA KOTA TANJUNGPINANG Dila Kharisma Aprilia; Armansyah Armansyah; Risnawati Risnawati; Eko Murti Saputra; Herman Herman
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.v9i1.5814

Abstract

Abstract: The aim of this research is to determine the influence of work motivation and organizational culture on the work discipline of the Tanjungpinang City Satpol PP. Researchers used a sample of 116 respondents using a saturated sampling technique. The method used in this research is a quantitative method. The object of this research is the Tanjungpinang City Satpol PP. Data collection was carried out by distributing questionnaires. Where respondents filled out a questionnaire with 17 statements relating to the variables being measured. The results of this research indicate that work motivation and organizational culture influence the work discipline of the Tanjungpinang City Satpol PP. Where work motivation and organizational culture are important elements in an organization/agency, because work motivation can create an organizational culture so that it can improve the work discipline of the Tanjungpinang City Satpol PP. Based on the research results, it was concluded that work motivation and organizational culture had a partial or simultaneous influence on the work discipline of the Tanjungpinang City Satpol PP. Keywords: Work Motivation, Organizational Culture, and Work Discipline. Abstrak: Tujuan dari penelitian ini adalah untuk mengetahui pengaruh motivasi kerja dan budaya organisasi terhadap disiplin kerja Satpol PP Kota Tanjungpinang. Peneliti menggunakan sampel sebanyak 116 orang responden dengan menggunakan teknik sampel jenuh. Metode yang digunakan dalam penelitian ini adalah metode kuantitatif. Objek penelitian ini adalah Satpol PP Kota Tanjungpinang. Pengumpulan data dilakukan dengan penyebaran kuesioner. Dimana responden mengisi kuesioner sebanyak 17 butir pernyataan yang berkaitan dengan variabel yang diukur. Hasil penelitian ini menunjukkan bahwa motivasi kerja dan budaya organisasi berpengaruh terhadap disiplin kerja Satpol PP Kota Tanjungpinang. Dimana dengan motivasi kerja dan budaya organisasi merupakan unsur penting di dalam sebuah organisasi/instansi, sebab dengan adanya motivasi kerja dapat menciptakan budaya organisasi sehingga dapat meningkatkan disiplin kerja Satpol PP Kota Tanjungpinang. Berdasarkan hasil penelitian disimpulkan bahwa motivasi kerja dan budaya organisasi berpengaruh secara parsial maupun simultan terhadap disiplin kerja Satpol PP Kota Tanjungpinang. Kata Kunci: Motivasi Kerja, Budaya Organisasi, Disiplin Kerja.
EXAMINING JOB DEMANDS, ENGAGEMENT, AND JOB SATISFACTION AMONG HOSPITAL STAFF Ummu Kalsum; Salwah Suardi; Indriani Mentaruk; Andi Nurul Azizah
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/vc906342

Abstract

Abstract: Quality healthcare can only be achieved through highly motivated and professionally competent workers. Hospitals are dynamic work environments, where healthcare workers face long hours, heavy workloads, and high pressure that can affect their engagement and job satisfaction. This study aims to analyze the effect of job demands (JD) on work engagement (WE) and its impact on job satisfaction (JS) among healthcare workers at Haji Hospital in Makassar, South Sulawesi Province. This study uses a quantitative approach with a cross-sectional design. A total of 244 respondents were selected using random quota sampling. The data were analyzed using Path Analysis with SPSS AMOS 26 software. The results showed that job demand had a significant effect on work engagement (p=0.028) and job satisfaction (p=0.027). However, there was no indirect effect of job demand on job satisfaction through work engagement (p=0.114). These findings indicate that increasing work engagement and job satisfaction among hospital staff is more effective through the direct influence of job demands. Employees tend to be more engaged and satisfied when faced with high job demands.   Keywords: Job demand; Work engagement; Job satisfaction; Health services; Hospitals   Abstrak: Pelayanan kesehatan yang berkualitas hanya dapat dicapai melalui keberadaan tenaga kerja yang memiliki motivasi tinggi dan kompetensi profesional. Rumah sakit merupakan lingkungan kerja yang dinamis, di mana tenaga kesehatan menghadapi jam kerja panjang, beban kerja berat, dan tekanan tinggi yang dapat memengaruhi keterlibatan serta kepuasan kerja. Penelitian ini bertujuan menganalisis pengaruh tuntutan pekerjaan (Job Demand/JD) terhadap keterlibatan kerja (Work Engagement/WE) dan dampaknya terhadap kepuasan kerja (Job Satisfaction/JS) pada tenaga kesehatan di RSUD Haji Provinsi Sulawesi Selatan. Penelitian ini menggunakan pendekatan kuantitatif dengan desain cross-sectional. Sebanyak 244 responden dipilih melalui metode random quota sampling. Data dianalisis menggunakan Path Analysis dengan perangkat lunak SPSS AMOS 26.  Hasil penelitian menunjukkan bahwa job demand berpengaruh signifikan terhadap work engagement (p=0,028) dan kepuasan kerja (p=0,027). Namun, tidak terdapat pengaruh tidak langsung antara job demand terhadap kepuasan kerja melalui work engagement (p=0,114). Temuan ini menunjukkan bahwa peningkatan work engagement dan kepuasan kerja staf Rumah sakit lebih efektif melalui pengaruh langsung dari job demand. Pegawai cenderung lebih terlibat dan puas ketika menghadapi job demand yang tinggi.   Kata kunci: Tuntutan Pekerjaan; Keterlibatan Kerja; Kepuasan Kerja; Tenaga Kesehatan; Rumah Sakit
MENELAAH KOMPLEKSITAS KEBIJAKAN DALAM PENGATURAN KONFLIK KEPENTINGAN DAN TINDAK PIDANA KORUPSI: STUDI PERBANDINGAN INDONESIA DAN SINGAPURA Rudi Pardede
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.v9i1.5816

Abstract

Abstract: This study examines the complexity of policies governing conflicts of interest and corruption between Indonesia and Singapore through a normative juridical approach. The study was conducted by examining the regulatory framework, institutions, and legal implementation in preventing the practice of abuse of public office. The results of the study indicate that the Indonesian regulatory framework, although comprehensive, tends to function as a legislative showcase without transformative power because it is trapped in a political dynamic that is permissive towards corruption. Instruments such as the Corruption Law, PP 53/2010, and the code of ethics for public officials are more symbolic than substantive, thus failing to change bureaucratic behavior that is still shaped by a culture of patronage and clientelism. In contrast, Singapore proves that simple but sharp regulations, such as the Prevention of Corruption Act, when supported by an independent anti-corruption agency (CPIB), indiscriminate law enforcement, and integrity-based bureaucratic incentives, can suppress corruption to a minimum level. However, Singapore's system remains subject to criticism, particularly regarding the risk of legal authoritarianism and vulnerability to cross-border global financial issues. Therefore, this study confirms that the effectiveness of corruption eradication is not determined by the number of regulations, but rather by political consistency, independent institutional design, and the internalization of integrity values within the bureaucratic culture. For Indonesia, an important lesson from Singapore is the urgency of simplifying regulations, strengthening the independence of anti-corruption institutions, and fostering a bureaucratic culture that rejects patronage. Keywords: complexity, nurative, legislative showcase, authoritarianism, internalization, bureaucracy Abstrak: Penelitian ini menelaah kompleksitas kebijakan pengaturan konflik kepentingan dan tindak pidana korupsi antara Indonesia dan Singapura melalui pendekatan yuridis normatif. Kajian dilakukan dengan menelaah kerangka regulasi, kelembagaan, serta implementasi hukum dalam mencegah praktik penyalahgunaan jabatan publik. Hasil Penelitian menunjukkan bahwa kerangka regulasi Indonesia, meskipun komprehensif, cenderung berfungsi sebagai legislative showcase tanpa daya transformasi karena terjebak dalam dinamika politik yang permisif terhadap korupsi. Instrumen seperti UU Tipikor, PP 53/2010, maupun kode etik pejabat publik lebih bersifat simbolik daripada substantif, sehingga gagal mengubah perilaku birokrasi yang masih dibentuk oleh budaya patronase dan clientelism. Sebaliknya, Singapura membuktikan bahwa regulasi yang sederhana namun tajam, seperti Prevention of Corruption Act. Jika ditopang lembaga antikorupsi independen (CPIB), penegakan hukum tanpa pandang bulu, dan insentif birokrasi berbasis integritas, mampu menekan korupsi hingga level minimal. Meski demikian, sistem Singapura tetap menyisakan kritik, terutama terkait risiko otoritarianisme hukum dan kerentanan terhadap isu keuangan global lintas batas. Dengan demikian, penelitian ini menegaskan bahwa efektivitas pemberantasan korupsi tidak ditentukan oleh banyaknya regulasi, melainkan oleh konsistensi politik, desain kelembagaan yang independen, serta internalisasi nilai integritas dalam budaya birokrasi. Bagi Indonesia, pelajaran penting dari Singapura adalah urgensi menyederhanakan regulasi, memperkuat independensi lembaga antikorupsi, dan menumbuhkan budaya birokrasi yang menolak patronase. Kata kunci: kompleksitas, nuratif, legislatif showcase, otoritarisme, internalisasi, birokrasi
PENYELESAIAN SENGKETA PENERTIBAN PENGGUNAAN TENAGA LISTRIK PADA PERUSAHAAN LISTRIK NEGARA DI UNIT INDUK DISTRIBUSI RIAU DAN KEPRI Muhammad Husni Armi; Indra Afrita; Hasan Basri
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.v9i1.5817

Abstract

Abstrack: This sociological legal research analyzes the settlement of Penertiban Pemakaian Tenaga Listrik (P2TL) disputes at PLN Riau and Riau Islands Distribution Unit, which is primarily conducted through non-litigation channels (internal mediation) and litigation as a last resort. The findings indicate that while procedures align with regulations, implementation remains ineffective in achieving justice due to low legal and technical literacy among customers, lack of procedural transparency, and a legal power imbalance between PLN and consumers. To address these obstacles, it is essential to enhance public education, strengthen PLN’s internal accountability, and provide proportional legal protection for customers to ensure a more transparent dispute resolution process oriented toward legal certainty. Keywords: Dispute Resolution, Electricity Use Inspection (P2TL), PT PLN (Persero), Sociological Jurisprudence. Abstrak: Penelitian hukum sosiologis ini menganalisis penyelesaian sengketa Penertiban Pemakaian Tenaga Listrik (P2TL) pada PLN Unit Induk Distribusi Riau dan Kepri yang dilakukan melalui jalur non-litigasi (mediasi internal) sebagai mekanisme utama dan jalur litigasi sebagai upaya terakhir. Hasil penelitian menunjukkan bahwa meskipun prosedur telah sesuai regulasi, pelaksanaannya belum efektif mencapai keadilan akibat rendahnya literasi hukum pelanggan, kurangnya transparansi prosedur, serta ketidakseimbangan posisi hukum antara PLN dan konsumen. Untuk mengatasi hambatan tersebut, diperlukan peningkatan edukasi masyarakat, penguatan akuntabilitas internal PLN, dan pemberian perlindungan hukum yang proporsional bagi pelanggan guna mewujudkan penyelesaian sengketa yang lebih transparan dan berorientasi pada kepastian hukum. Kata Kunci: Penyelesaian Sengketa, Penertiban, Tenaga Listrik. Hukum Sosiologi.

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