Abstract:Giving awards is essential to motivate students; however, selecting outstanding students at the junior high school level is often conducted manually and subjectively, which can lead to unfairness and prolonged processing…
time. This study develops a Decision Support System (DSS) that integrates the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support objective and transparent student selection. A quantitative descriptive approach was employed, with data collected through questionnaires, interviews, and documentation at two state junior high schools in Banjarmasin City. Seven assessment criteria were applied: attendance, behavior, uniform neatness, extracurricular participation, academic grades, competition achievements, and disciplinary records. AHP was used to determine the weight of each criterion, while TOPSIS ranked students based on these weights. The web-based system was developed using PHP and MySQL and evaluated using the Technology Acceptance Model (TAM). Results show that academic grades had the highest weight (28.5%), followed by attendance (22.3%) and competition performance (15.2%). The TAM evaluation yielded average scores of 4.32 for Perceived Ease of Use, 4.40 for Perceived Usefulness, 4.15 for Attitudes Towards Use, and 4.28 for Behavioral Intention to Use. The DSS produces accurate rankings, is well-received by users, and offers an efficient, fair, and replicable solution for data-driven educational governance in the digital era.
Abstract:Abstract: Non-performing loans remain one of the main challenges faced by cooperatives, particularly when the loan eligibility assessment process is still conducted manually. This traditional approach tends to be time consuming,…
nsuming, subjective, and prone to inaccurate decisions. This study aims to develop a predictive model for borrower eligibility using the Support Vector Machine (SVM) algorithm as a more efficient and objective machine learning-based solution. A total of 1,000 loan history records were processed using RapidMiner software, taking into account variables such as salary, years of employment, loan amount, monthly installment, employment status, monthly expenses, number of dependents, housing status, age, and collateral value. The model’s performance was evaluated using a confusion matrix and classification metrics including accuracy, precision, recall, and kappa. The results indicate that the SVM model achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of 90,08%, reflecting a strong performance in classifying borrower eligibility. The application of this method makes a significant contribution to the development of data driven decision support systems within cooperative environments. This finding expands the scientific understanding in the field of microfinance and supports the implementation of artificial intelligence technologies in making decisions that are more precise, rapid, and accurate.
Keywords: cooperative; eligibility prediction; machine learning; non-performing loan; SVM
Abstrak: Kredit macet merupakan salah satu permasalahan utama yang dihadapi koperasi, terutama ketika proses penilaian kelayakan peminjam masih dilakukan secara manual. Pendekatan ini cenderung lambat, subjektif, dan berisiko menghasilkan keputusan yang kurang akurat. Penelitian ini bertujuan untuk membangun model prediksi kelayakan peminjam menggunakan algoritma Support Vector Machine (SVM) sebagai solusi berbasis machine learning yang lebih efisien dan objektif. Sebanyak 1.000 data riwayat pinjaman diolah menggunakan tools RapidMiner dengan mempertimbangkan variabel: gaji, lama bekerja, besar pinjaman, angsuran per bulan, status pegawai, pengeluaran bulanan, jumlah tanggungan, status rumah, umur, dan nilai jaminan. Evaluasi model dilakukan menggunakan confusion matrix dan metrik klasifikasi seperti akurasi, presisi, recall, dan kappa. Hasil menunjukkan bahwa model SVM mencapai akurasi 90,05%, presisi 90,13%, recall 90,05%, dan f1 score 90,08%, yang mencerminkan performa model yang sangat baik dalam mengklasifikasikan kelayakan peminjam. Penerapan metode ini memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan berbasis data di lingkungan koperasi. Temuan ini memperluas wawasan keilmuan di bidang keuangan mikro dan mendukung penerapan teknologi kecerdasan buatan dalam pengambilan keputusan yang lebih tepat, cepat, dan akurat.
Kata Kunci: koperasi; kredit macet; machine learning; prediksi kelayakan; SVM
Abstract:Abstract: This research is driven by the challenges faced by Universitas Lancang Kuning (UNILAK) in attracting applicants amidst intense competition, especially after the government's policy opened independent pathways to…
o State Universities (PTN) from 2022-2023, which impacted private university applicant numbers. To address this and support strategic planning, this study aims to predict the trend of prospective students applying to all study programs at UNILAK for the period 2025-2027. Two time series models were employed: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Applicant data from 2019 to 2024 was used to build the model. The Augmented Dickey-Fuller (ADF) test confirmed the data's stationarity with a p-value of 0.0. ACF and PACF analyses determined the ARIMA parameters as p=1, d=1, q=1. The LSTM model was trained to capture more complex data patterns. ARIMA predictions for 2025, 2026, and 2027 are 3298.66, 3362.33, and 3371.30, respectively. LSTM predictions for the same years are 3335.64, 3476.52, and 3518.42. Evaluation using Root Mean Squared Error (RMSE) showed ARIMA (RMSE=588.72) to be more accurate than LSTM (RMSE=653.96). Nevertheless, LSTM provided a more optimistic prediction. This study concludes that ARIMA is better suited for short-term planning, while LSTM can be used for more ambitious long-term strategies.
Keywords: arima; LSTM; applicants; prediction; university
Abstrak: Penelitian ini didorong oleh tantangan Universitas Lancang Kuning (UNILAK) dalam menarik pendaftar di tengah persaingan ketat, khususnya setelah kebijakan pemerintah membuka jalur mandiri ke Perguruan Tinggi Negeri (PTN) sejak 2022-2023, yang menyebabkan penurunan jumlah pendaftar di universitas swasta. Untuk mendukung perencanaan strategis, studi ini bertujuan memprediksi tren jumlah calon mahasiswa yang mendaftar ke seluruh program studi di UNILAK untuk periode 2025-2027.Dua model deret waktu digunakan: ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Data jumlah pendaftar dari 2019 hingga 2024 digunakan untuk membangun model. Uji Augmented Dickey-Fuller (ADF) menunjukkan data stasioner dengan p-value 0,0. Analisis ACF dan PACF menentukan parameter ARIMA sebagai p=1, d=1, q=1. Model LSTM dilatih untuk menangkap pola data yang lebih kompleks.Prediksi ARIMA untuk 2025, 2026, dan 2027 adalah 3298.66, 3362.33, dan 3371.30. Prediksi LSTM untuk tahun yang sama adalah 3335.64, 3476.52, dan 3518.42. Evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan ARIMA (RMSE=588.72) lebih akurat daripada LSTM (RMSE=653.96). Meskipun demikian, LSTM memberikan prediksi yang lebih optimis. Studi ini menyimpulkan ARIMA lebih cocok untuk perencanaan jangka pendek, sementara LSTM dapat digunakan untuk strategi jangka panjang yang ambisius.
Kata kunci: arima; LSTM; pendaftar; prediksi; universitas
Abstract:Abstract: The rapid population growth in Tanjung Tiram District, primarily driven by increased in-migration, demands an accurate forecasting system to support effective and sustainable development planning. This study aims…
ms to predict population growth in Tanjung Tiram District in 2024 using the Least Square method. The analysis covers birth, arrival, and migration data from 2019 to 2023. The results show that the Least Square method successfully predicts 936 births, 104 arrivals, and 142 migrations in 2024, with a very low error rate: MAPE for births is 0.01%, arrivals 0.12%, and migrations 0.04%. These research demonstrate that the Least Square method can effectively support data-driven development policies and improve the accuracy of public service distribution planning.
Keywords: forecasting; least square method; population growth; tanjung tiram.
Abstrak: Pertumbuhan penduduk yang pesat di Kecamatan Tanjung Tiram, terutama akibat peningkatan migrasi masuk, menuntut adanya sistem prediksi yang akurat untuk mendukung perencanaan pembangunan yang efektif dan berkelanjutan. Penelitian ini bertujuan untuk memprediksi pertumbuhan penduduk di Kecamatan Tanjung Tiram pada tahun 2024 menggunakan pendekatan metode Least Square. Data yang dianalisis mencakup jumlah kelahiran, kedatangan, dan perpindahan penduduk dari tahun 2019 hingga 2023. Hasil penelitian menunjukkan bahwa metode Least Square mampu memprediksi jumlah kelahiran sebesar 936 jiwa, kedatangan 104 jiwa, dan perpindahan 142 jiwa pada tahun 2024, dengan tingkat kesalahan yang sangat rendah: MAPE untuk kelahiran sebesar 0,01%, kedatangan 0,12%, dan perpindahan 0,04%. Penelitian ini membuktikan bahwa metode Least Square dapat digunakan secara efektif untuk mendukung penyusunan kebijakan pembangunan yang berbasis data dan memperkuat akurasi distribusi layanan publik.
Kata kunci: metode least square; peramalan; pertumbuhan penduduk; tanjung tiram.
Abstract:Abstract: The advancement of network security faces growing challenges as cyberattacks become more sophisticated. Traditional rule-based systems struggle with zero-day attacks and obfuscation techniques. This study examines…
nes the development trends of AI-based algo-rithms, particularly machine learning and deep learning, in threat detection. A literature review evaluates AI-driven approaches, including support vector machines, random for-est, deep neural networks, convolutional neural networks, and reinforcement learning. Findings show that AI enhances detection accuracy, adaptability, and reduces false posi-tives. Machine learning efficiently classifies known attacks, while deep learning excels in identifying complex patterns such as distributed denial-of-service and advanced persis-tent threats. Unsupervised learning improves anomaly detection without labeled data. However, AI models require high-quality data, substantial computational resources, and remain vulnerable to adversarial attacks. Despite these challenges, AI provides a dynam-ic and adaptive security solution, surpassing traditional systems. Future research should enhance AI scalability and resilience for evolving cybersecurity threats.
Keywords: anomaly detection; artificial intelligence; deep learning; machine learning; network security
Abstrak: Perkembangan keamanan jaringan menghadapi tantangan yang semakin besar seiring meningkatnya kompleksitas serangan siber. Sistem berbasis aturan tradisional kesulitan mendeteksi zero-day attack dan teknik penyamaran. Penelitian ini mengkaji tren pengembangan algoritma berbasis AI, khususnya machine learning dan deep learning, dalam deteksi ancaman. Literature review mengevaluasi pendekatan berbasis AI, termasuk support vector machines, random forest, deep neural networks, convolutional neural networks, dan reinforcement learning. Hasil penelitian menunjukkan bahwa AI meningkatkan akurasi deteksi, adaptabilitas terhadap ancaman baru, serta mengurangi false positive. Machine learning efektif mengklasifikasikan serangan yang telah diketahui, sementara deep learning unggul dalam mengenali pola kompleks seperti distributed denial-of-service dan advanced persistent threats. Unsupervised learning meningkatkan deteksi anomali tanpa memerlukan data berlabel. Namun, AI masih bergantung pada data berkualitas tinggi, sumber daya komputasi besar, dan rentan terhadap adversarial attack. Meskipun demikian, AI menawarkan solusi keamanan yang lebih dinamis dan adaptif dibandingkan sistem tradisional. Penelitian selanjutnya perlu difokuskan pada peningkatan skalabilitas dan ketahanan AI dalam menghadapi ancaman siber yang terus berkembang.
Kata kunci: deteksi anomali; jaringan keamanan; kecerdasan buatan; pembelajaran dalam; pembelajaran mesin
Abstract:Abstract: This research was conducted in the Bangka Belitung region specifically observing what criteria could support and hinder the development of home industries. The home industry is a small-scale industry that is generally…
nerally carried out in the family sphere and is driven by women. Home industries need to receive support from the provincial government to survive and increase the scale of production. To make support for home industries more targeted, this research summarizes several criteria related to the conditions of home industries and the businesses they run. In Bangka Belitung Province there are several types of businesses that are run on a home industry scale. Based on the condition of the home industry which has multiple criteria and multiple alternatives, this research uses the Analytical Hierarchy Process method and Expert Choice software as data processing aids. The results of data processing show that industrial business is the highest alternative with a weight of 24.7% and the highest criterion is strategy at 23.7%. These results indicate that the largest portion of home industry business actors is engaged in the industrial sector and to encourage the progress of the home industry the most important factor is strategy, namely understanding the type of business they are involved in, labor-intensive industries, paying attention to production factors, electricity capacity, and business operations. While the next stage is to design a user interface for a web-based system as a means of collecting home industry data.
Keywords: AHP; collecting home industry data; home industries
Abstrak: Penelitian ini dilakukan di wilayah Bangka Belitung secara khusus melihat kriteria apa saja yang dapat mendukung dan menghambat perkembangan industri rumah tangga. Industri rumah tangga merupakan industri kecil yang umumnya dilakukan dalam lingkup keluarga dan digerakkan oleh perempuan. Industri rumah tangga perlu mendapat dukungan dari pemerintah provinsi agar bisa bertahan dan meningkatkan skala produksinya. Agar dukungan industri rumah tangga lebih tepat sasaran, penelitian ini merangkum beberapa kriteria terkait kondisi industri rumah tangga dan usaha yang dijalankannya. Di Provinsi Bangka Belitung terdapat beberapa jenis usaha yang dijalankan dalam skala industri rumah tangga. Berdasarkan kondisi industri rumah tangga yang memiliki banyak kriteria dan banyak alternatif, maka penelitian ini menggunakan metode Analytical Hierarchy Process dan software Expert Choice sebagai alat bantu pengolahan data. Hasil pengolahan data menunjukkan bahwa bisnis industri merupakan alternatif tertinggi dengan bobot 24,7% dan kriteria tertinggi adalah strategi 23,7%. Hasil tersebut menunjukkan bahwa porsi terbesar pelaku usaha industri rumah tangga bergerak di sektor industri dan untuk mendorong kemajuan industri rumah tangga faktor yang paling penting adalah strategi yaitu memahami jenis usaha yang digeluti, industri padat karya, pembayaran, memperhatikan faktor produksi, kapasitas listrik, dan operasional usaha. Sedangkan tahap selanjutnya adalah merancang antarmuka untuk sistem berbasis web sebagai sarana pendataan industri rumahan.
Kata kunci: AHP; industri rumahan; pendataan industri rumahan
Abstract:Abstrack: The ability of computers to remember and store information properly can be utilized without having to depend on deficiencies that humans have, such as hunger, thirst and emotions that can be felt at any time as…
as humans. Except for electrical energy, all human weaknesses in remembering something can be done by a computer without obstacles. This forward tracking is a data-driven approach. In this approach tracking starts with input information, and then tries to draw conclusions. The basic concept of an expert system where the user submits information or facts to the expert system then the user receives a solution or answer from the expert system. Expert System Application to Detect Heavy Equipment Damage at PT.Tamako Raya Perdana was made so that it can help Heavy Equipment Operators (Excavators) or Companies, and may be able to help Mechanics if they forget the mechanism of action on Heavy Equipment (Excavators). So they no longer need to bother to get the information they need in handling problems in the Excavator.
Keyword: expert system, forward chaining, heavy equipment
Abstrak: Kemampuan Komputer untuk mengingat dan menyimpan informasi dengan baik dapat dimanfaatkan tanpa harus bergantung kepada kekurangan-kekurangan yang dimiliki manusia, seperti lapar, haus dan emosi yang sewaktu-waktu bisa dirasakan seperti pada manusia. Kecuali karena energy listrik, semua kelemahan manusia dalam mengingat sesuatu bisa dilakukan oleh komputer tanpa kendala. Pelacakan kedepan atau runut maju ini adalah pendekatan yang dimotori data (data-driven). Dalam pendekatan ini pelacakan dimulai dari informasi masukan, dan selanjutnya mencoba menggambarkan kesimpulan. Konsep dasar sistem pakar dimana pengguna menyampaikan informasi atau fakta untuk sistem pakar kemudian pengguna menerima solusi atau jawaban dari sistem pakar. Aplikasi Sistem Pakar Untuk Mendeteksi Kerusakan Alat Berat Pada PT.Tamako Raya Perdana ini dibuat agar dapat membantu para Operator Alat Berat (Excavator) atau Perusahaan, dan mungkin dapat membantu Mekanik bila lupa akan mekanisme kerja pada Alat Berat (Excavator). Sehingga mereka tidak perlu lagi bersusah payah untuk mendapatkan informasi yang mereka butuhkan dalam menangani masalah-masalah pada Excavator tersebut.
Kata kunci : sistem pakar, runut maju, alat berat
Abstract:This study examines how employees perceive and trust Algorithmic Performance Appraisal (APA) in digital-native and technology-driven companies. Adopting Organizational Justice Theory, the Trust in Technology Framework, and…
nd Cognitive Appraisal Theory, the research explores both the direct and indirect effects of APA on employee trust, with Perceived Procedural Fairness (PPF) as a mediating variable. The study uses a quantitative, cross-sectional approach, collecting data from 200 employees in technology-based organizations and analyzing the data with Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that APA significantly enhances both procedural fairness and employee trust, with PPF playing a partial mediating role in this relationship. These findings underscore the importance of transparency, procedural legitimacy, and avenues for employee voice in cultivating trust in algorithmic systems. The study’s theoretical contribution lies in its integration of multiple perspectives on trust and fairness within algorithmic HR management. The practical implication calls for the careful design and implementation of APA systems that employees perceive as fair and trustworthy. Future research should investigate these relationships in longitudinal and multi-contextual settings to deepen the understanding of trust dynamics in evolving AI-mediated work environments.
Abstract:The rapid advancement of digital technology and artificial intelligence (AI) in the twenty-first century has fundamentally transformed the structure of modern constitutional governance. Digitalization has reshaped the interactions…
teractions between governments and citizens, altered the patterns of political participation, and presented major challenges to constitutional principles. This study aims to analyze the implications of technological development for constitutional law and propose the concept of a digital constitution as an adaptive framework in the AI era. This study employs a normative legal method, using both conceptual and statutory approaches. Legal materials consist of primary, secondary, and tertiary sources, which are analyzed qualitatively and descriptively. The findings reveal that, while digital transformation enhances governmental efficiency and public transparency, it also generates serious risks, including data misuse, digital surveillance, and political disinformation. These dynamics demand a reinterpretation of constitutional norms to protect citizens' digital rights in cyberspace. The concept of a digital constitution is proposed as a normative response that integrates digital rights into constitutional rights and reaffirms the principle of the rule of law within technological governance. To achieve a democratic and just constitutional order, it is crucial to strengthen regulatory frameworks, ensure algorithmic accountability, and foster collaboration among state institutions, civil society, and the private sector. Ultimately, constitutional law must evolve into an adaptive, transparent, and fair system that can address the profound challenges of the digital and AI-driven era.
Abstract:This study investigates the communicative practices of youth volunteers involved in Surabaya’s Kelurahan Bersinar anti-drug initiative, highlighting the role of grassroots messaging in shaping public health discourse. Grounded…
Grounded in framing theory, narrative theory, and social capital theory, the research explores how volunteers construct persuasive anti-drug messages through culturally rooted storytelling, emotional framing, and symbolic communication. Using a qualitative case study approach, data were gathered through interviews, focus groups, observations, and campaign materials with 18 youth volunteers. The findings reveal that volunteers effectively used personal experiences and local narratives to foster trust and collective identity, transforming individual challenges into shared stories of hope and resistance. Emotional strategies, such as gain-framed and fear-based appeals, were deliberately crafted to trigger empathy and promote behavioral change. Volunteers also capitalized on their deep integration within community networks to enhance message credibility and reach, cultivating both bonding and bridging social capital. The study challenges top-down approaches in health campaigns, illustrating the power of peer-led, culturally resonant communication. It contributes a layered communication model for grassroots health advocacy and underscores the transformative impact of narrative-driven volunteer engagement. The study recommends integrating cultural framing in training programs and exploring digital channels to scale grassroots messaging in future research.