Abstract:Daiama Tourism Village in Rote Ndao Regency possesses significant natural and cultural tourism potential; however, its tourism promotion remains suboptimal, resulting in fluctuating tourist visitation rates. This study aims…
ims to analyze promotional activities, identify factors influencing promotional effectiveness, and formulate promotional strategies to increase tourist visits. The research employed a qualitative descriptive method through observation, interviews, literature review, and documentation, supported by interactive data analysis and triangulation techniques. The findings reveal that promotional efforts still rely primarily on annual festivals and direct interpersonal communication without systematic support from planned digital media. This condition has contributed to fluctuations in tourist arrivals, increasing from 587 visitors in 2022 to 711 visitors in 2023, before declining to 503 visitors in 2024 and 468 visitors in 2025. Constraining factors include limited human resources, weak digital media management, and the underdeveloped institutional capacity of the Tourism Awareness Group (Pokdarwis). The study concludes that promotional strategies should be directed toward digital marketing, strengthening cultural identity and local storytelling, as well as enhancing community and institutional capacity. The novelty of this study lies in the development of a sustainable promotional model that integrates digital marketing, cultural storytelling, community participation, and the strengthening of Pokdarwis institutions in accordance with the characteristics of Daiama Tourism Village as a nature- and culture-based tourism destination.
Abstract:Students in early adulthood often face academic, social, and personal pressures that cause fluctuations in mood and affect mental health and psychological well-being. The main issue addressed in this study is students’ low…
low ability to recognize, understand, and manage emotions adaptively, highlighting the need for effective strategies to enhance emotional awareness. This research investigates the effectiveness of self-reflection and journaling in improving students’ mood awareness. A combination of descriptive qualitative and quasi-experimental pretest-posttest designs was used, with instruments including mood scales, reflection guidelines, and students’ daily journals. Results showed a 32% increase in the average mood awareness score, a decrease in students with low awareness from 64% to 18%, and an increase in high awareness from 14% to 56%. Active participation was observed in 78% of students, while 85% consistently documented their emotional experiences, 88% reported feeling relieved, and 76% could identify recurring emotional patterns. The intervention also promoted the development of more adaptive self-regulation strategies, such as time management, adequate rest, and social support, increasing from 21% before the intervention to 69% afterward. Sustainability of practice was indicated by 61% of students intending to continue journaling independently and 57% integrating it into their daily routines. The novelty of this study lies in the integration of self-reflection and journaling as a practical, systematic, and sustainable approach to enhance emotional awareness and psychological well-being. The findings confirm that a simple yet structured intervention can create significant improvements in emotion management and adaptive behaviors among students.
Abstract:This study aims to empirically analyze the effect of exchange rates and interest rates on the stock prices of banking companies listed on the Indonesia Stock Exchange during the 2020-2024 period. The research employs a quantitative…
uantitative approach with a causal-associative design and utilizes secondary data obtained from official publications of Bank Indonesia (BI) and the Indonesia Stock Exchange (IDX). The research population consists of 45 banking companies, from which 20 companies meeting the sampling criteria were selected using purposive sampling. Data analysis was conducted comprehensively through descriptive analysis, classical assumption tests, multiple linear regression, and hypothesis testing with the assistance of SPSS software. The findings reveal that, partially, exchange rates have a significant negative effect on stock prices, and interest rates also exert a significant negative effect. Furthermore, simultaneous testing confirms that both independent variables significantly and negatively influence banking stock prices. These results highlight that macroeconomic volatility, particularly exchange rate fluctuations and interest rate movements, are crucial determinants to be considered in capital market analysis. Academically, this study contributes to the literature on the relationship between macroeconomic indicators and stock price dynamics, while practically, the results may serve as a reference for investors in making investment decisions, for policymakers in formulating economic stabilization strategies, and for banking institutions in anticipating market risks arising from both global and domestic economic uncertainty.
Abstract:The rapid growth of the online transportation sector in Indonesia has driven a significant transformation in urban mobility systems, including in Denpasar City. One of the impacts of this development is the increasing competition…
mpetition among driver-partners, which directly affects fluctuations in their income. This study aims to empirically analyze the influence of competition level, working hours duration, and fare policies on the income of GoRide partners in Denpasar City. A quantitative approach was used in this research, with the population consisting of all active GoRide driver-partners in the area. The sample was determined using the Slovin formula with a 10% significance level, resulting in 67 randomly selected respondents. Data were collected through the distribution of structured questionnaires and analyzed using multiple linear regression methods to examine the effect of each independent variable on income as the dependent variable. The results show that the competition variable has a significant negative effect on partner income, indicating that the higher the level of competition, the lower the income earned. Conversely, working hours have a positive and significant effect, indicating that increased working duration directly contributes to higher income. Meanwhile, fare policy shows a positive but insignificant effect, meaning that although there is a statistically positive relationship, the effect is not strong enough to significantly impact income. These findings highlight the importance of efficient management of working hours and strategic adaptation to competition levels as efforts to maintain the economic sustainability of driver-partners. In addition, platform providers are advised to periodically review fare policies to ensure they remain aligned with market dynamics and partner needs, in order to maintain a sustainable income balance.
Abstract:Stock prices represent the market’s consensus regarding the fair value of a business entity, formed through a rational assessment of both financial and non-financial information. Within the frameworks of signaling theory…
ry and market efficiency, financial ratios serve as fundamental indicators reflecting a company's ability to generate profit, manage assets, and maintain short-term liquidity stability. When the market faces systemic disruptions, such as the COVID-19 pandemic, and transitions toward post-pandemic normalization, investor sensitivity to internal indicators tends to increase due to heightened external uncertainty. Consequently, a deep understanding of the internal determinants of stock prices becomes increasingly critical for data-driven investment decision-making. This study aims to empirically examine the effect of the Current Ratio (CR), Return on Assets (ROA), and Total Asset Turnover (TATO) on stock prices, both partially and simultaneously, in companies listed on the Indonesia Stock Exchange (IDX) during the 2020-2024 period. A quantitative approach was employed, using a descriptive-verificative design and secondary data drawn from audited financial statements and annual closing stock prices. The sample consisted of 91 companies selected through purposive sampling, and the data were analyzed using multiple linear regression with SPSS version 27. The analysis results indicate that all three independent variables have a positive and significant impact on stock prices, both individually and collectively. This study contributes to the capital market literature by highlighting the relevance of financial ratios in explaining stock price fluctuations in the post-pandemic period, a topic that remains underexplored in the context of developing countries.
Abstract:The rapid development of globalization compels companies to enhance the quality of their products and services to compete in the market. The primary goal of companies is to maximize corporate value, which reflects the well-being…
ll-being of shareholders and attracts investors’ interest. Corporate value is often gauged by the stock price in the capital market, making it crucial for companies to plan sound financial strategies. This research focuses on the banking sector listed on the Indonesia Stock Exchange (IDX) from 2021 to 2023, evaluating the influence of investment policy, dividend policy, and profitability on corporate value. The study employs a descriptive quantitative method using secondary data from the companies’ financial reports. Multiple linear regression analysis is used to assess the relationships between these variables. The results indicate that investment policy and profitability have a positive but not significant effect on corporate value, while dividend policy has a significant positive impact. Corporate value is also affected by stock price fluctuations, which are often inconsistent. These findings affirm that dividend policy is a crucial factor in enhancing corporate value, while investment policy and profitability, although important, do not have a significant impact within the study period. Overall, these independent variables collectively have a significant influence on corporate value in the banking sector on the IDX. This research provides insights for company management in formulating effective strategies to increase corporate value.
Abstract:Abstract: Motorcycle sales are one of the key indicators of growth in Indonesia’s automotive industry. CV. Honda Karya Utama, as an authorized Honda motorcycle dealer, faces challenges due to unstable monthly sales fluctuations.…
ctuations. This uncertainty complicates stock planning, marketing strategy formulation, and may lead to potential losses caused by overstocking or understocking. To address these issues, this study aims to analyze the relationship between time and sales volume and to develop a predictive model that can assist management in making more effective business decisions.
The research was conducted using a quantitative method and a machine learning approach by applying a linear regression algorithm, implemented through the Python programming language and a MySQL database. The dataset used consists of monthly sales data over one year, analyzed to predict future sales trends. The results show that the linear regression algorithm can predict sales trends with a good level of accuracy, achieving evaluation values of MAPE 1.72%, MSE 1.76%, and RMSE 0.57. The developed model assists management in formulating marketing strategies, optimizing inventory planning, and minimizing financial risks. Therefore, linear regression can serve as an effective analytical tool to support strategic business decision-making at CV. Honda Karya Utama.
Keywords: motorcycle sales; linear regression; prediction; sales analysis
Abstrak: Penjualan sepeda motor merupakan salah satu indikator penting dalam pertumbuhan industri otomotif di Indonesia. CV. Honda Karya Utama sebagai dealer resmi sepeda motor Honda menghadapi tantangan berupa fluktuasi penjualan yang tidak stabil setiap bulannya. Ketidakpastian ini menyebabkan kesulitan dalam perencanaan stok, penentuan strategi pemasaran, serta berpotensi menimbulkan kerugian akibat kelebihan atau kekurangan persediaan. Untuk mengatasi permasalahan tersebut, penelitian ini bertujuan untuk menganalisis hubungan antara waktu dan jumlah penjualan serta membangun model prediksi yang dapat membantu manajemen dalam pengambilan keputusan bisnis yang lebih efektif. Penelitian dilakukan dengan metode kuantitatif dan pendekatan machine learning menggunakan algoritma regresi linier, dengan implementasi berbasis bahasa pemrograman Python dan basis data MySQL. Data yang digunakan berupa data penjualan bulanan selama satu tahun, yang kemudian dianalisis untuk memprediksi tren penjualan pada periode berikutnya. Hasil penelitian menunjukkan bahwa regresi linier mampu memprediksi tren penjualan dengan tingkat akurasi yang baik, dengan nilai evaluasi MAPE sebesar 1,72%, MSE 1,76%, dan RMSE 0,57. Model ini membantu manajemen dalam menyusun strategi pemasaran, mengoptimalkan perencanaan stok, serta meminimalkan risiko kerugian. Dengan demikian, regresi linier dapat menjadi alat bantu yang efektif dalam mendukung keputusan bisnis di CV. Honda Karya Utama.
Kata kunci: penjualan sepeda motor; regresi linier; prediksi; analisis penjualan
Abstract:Abstract: Air quality in urban areas is becoming an increasingly important issue considering its impact on human health and the environment. The rapid increase in air pollution requires effective methods to predict air quality…
uality in order to take appropriate mitigation measures. This study aims to analyze the use of Neural Network (NN) algorithms in predicting air quality in cities. The method used is the application of the NN model, especially the Multilayer Perceptron (MLP), which is trained using historical air quality data such as dust particle levels (PM10, PM2.5), carbon monoxide (CO) gas, and temperature. The data used in this study came from urban air quality monitoring stations collected over a period of time. The results show that the Neural Network algorithm can provide quite accurate predictions of air quality with a low Mean Absolute Error (MAE) value, showing the effectiveness of the model in predicting f fluctuations in air quality. The conclusion of this study is that Neural Network algorithms, specifically MLPs, are an effective tool for air quality prediction, which can be used as a basis for urban air quality management policies.
Keywords: air quality; neural network; prediction; multilayer perceptron (MLP)
Abstrak: Kualitas udara di perkotaan menjadi isu yang semakin penting mengingat dampaknya terhadap kesehatan manusia dan lingkungan. Peningkatan polusi udara yang pesat memerlukan metode yang efektif untuk memprediksi kualitas udara guna mengambil langkah mitigasi yang tepat. Penelitian ini bertujuan untuk menganalisis penggunaan algoritma Neural Network (NN) dalam memprediksi kualitas udara di perkotaan. Metode yang digunakan adalah penerapan model NN, khususnya Multilayer Perceptron (MLP), yang dilatih menggunakan data kualitas udara historis seperti kadar partikel debu (PM10, PM2.5), gas karbon monoksida (CO), dan suhu. Data yang digunakan dalam penelitian ini berasal dari stasiun pemantauan kualitas udara di perkotaan yang dikumpulkan selama periode waktu tertentu. Hasil penelitian menunjukkan bahwa algoritma Neural Network dapat memberikan prediksi yang cukup akurat terhadap kualitas udara dengan nilai Mean Absolute Error (MAE) yang rendah, menunjukkan efektivitas model dalam memprediksi fluktuasi kualitas udara. Simpulan dari penelitian ini adalah bahwa algoritma Neural Network, khususnya MLP, merupakan alat yang efektif untuk prediksi kualitas udara, yang dapat digunakan sebagai dasar untuk kebijakan pengelolaan kualitas udara di perkotaan
Kata kunci: kualitas udara; neural network; prediksi; multilayer perceptron (MLP)
Abstract:Abstract: This research will discuss predictions of demand for robes at the Fashion Gallery. The problem that occurs is that there is often a buildup in the number of robes provided in shops, thereby increasing additional…
l expenses, such as storage costs and demand costs. On the other hand, if there is too little stock of robes, it will result in losses because potential buyers do not get the items they need. In this case, the forecasting system will be used as a tool to solve the problem of robe accumulation. The WMA method prioritizes the most recent data points while smoothing out fluctuations. The advantage of this method is increased responsiveness to recent changes in demand patterns, contributing to more accurate forecasts by considering current information more prominently than historical data. By implementing the WMA method, it is hoped that the accumulation of robes can be avoided. From the results of prediction calculations in January 2024, it is predicted that there will be 143 adult robes, with a MAD of 7.685185, MSE 74.79938 and MAPE 5%. For small children's robes, it is predicted that there will be 56 shirts, with MAD of 8.388889, MSE of 91.16358 and MAPE of 12%. It can be concluded that the WMA method can make it easier for Fashion Gallery to predict demand for adult and children's robes in the following month.
Keywords: forecasting; robe request; weighted moving average
Abstrak: Dalam penelitian ini akan membahas prediksi permintaan baju gamis pada Fashion Gallery. Permasalahan yang terjadi sering terjadinya penumpukan pada jumlah baju gamis yang disediakan pada toko, sehingga meningkatkan beban tambahan, seperti biaya penyimpanan dan biaya permintaan. Sebaliknya, apabila stok gamis yang terlalu sedikit, mengakibatkan kerugian yang disebabkan karena calon pembeli tidak mendapatkan barang yang dibutuhkan. Dalam hal ini sistem forecasting akan digunakan sebagai alat bantu dalam menyelesaikan permasalahan penumpukan baju gamis. Metode WMA, lebih mementingkan titik data terkini sambil memperlancar fluktuasi. Kelebihan metode ini meningkatkan daya tanggap terhadap perubahan pola permintaan terkini, berkontribusi pada perkiraan yang lebih akurat dengan mempertimbangkan informasi terkini secara lebih menonjol dibandingkan data historis. Dengan diterapkannya metode WMA, diharapkan dapat menghindari terjadinya penumpukan baju gamis. Dari hasil perhitungan prediksi pada bulan Januari 2024, untuk baju gamis dewasa diprediksi sebanyak 143 baju, dengan MAD sebesar 7.685185, MSE 74.79938 dan MAPE 5%. Untuk baju gamis anak kecil diprediksi sebanyak 56 baju, dengan MAD sebesar 8.388889, MSE 91.16358 dan MAPE sebesar 12%. Dapat disimpulkan bahwa metode WMA dapat memudahkan Fashion Gallery dalam meramalkan permintaan baju gamis dewasa dan anak-anak pada bulan berikutnya.
Kata Kunci: forecasting; permintaan baju gamis; weighted moving average
Abstract:Kutai Kartanegara Regency, East Kalimantan Province, has long depended on coal mining and petroleum as its primary economic drivers. This dependence creates structural vulnerability to global commodity price fluctuations…
and long-term sustainability challenges. This article presents a systematic literature review examining tourism as an alternative sector for regional economic transformation in Kutai Kartanegara. Drawing on 15 peer-reviewed studies from 2016–2024, the review synthesizes evidence on: (1) the role of tourism-led growth and its multiplier effects on local economies; (2) community-based tourism (CBT) as an inclusive development model; (3) structural transformation theory as a framework for economic diversification; and (4) the specific potentials and constraints facing tourism development in resource-dependent regions. The review finds that tourism exhibits significant forward and backward economic linkages, stimulates SME growth and creative economy, and can meaningfully reduce reliance on extractive industries. Key barriers include infrastructure gaps, limited human resource capacity, and suboptimal governance coordination. The findings provide a theoretical and empirical foundation for the ongoing field research into tourism's transformative role in Kutai Kartanegara's regional economy.