70 research outputs found
PENGEMBALIAN STATUS HUKUM TANAH ULAYAT ATAS HAK GUNA USAHA
No full textLand tenure by the state does not mean possessed, but as the name of the land, grants only the right of access to land which can be given to the land, its designation, use and maintenance and its arrangement to the law and the relations it can do to those lands. Article 3 of the BAL is indeed available the term "customary rights and similar rights". This paper is a normative juridical study, which will discuss the status of ulayat right to the right of business in Indonesia. In 2012, the Constitutional Court granted most of the material of Law No. 41 of 1999 on Forests petitioned for the Alliance of Indigenous Peoples of the Archipelago (AMAN) and two indigenous communities namely Kanegerian Kuntu and Kasepuhan Cisitu. After the issuance of Regulation of the Minister of Agrarian Affairs No. 5 of 1999, the status of land of Hak Guna Usaha derived from customary community land from a limited period of time or Hak Guna Usaha which abolishes based on applicable provisions, which then the land is re-paid into the community ulayat right customary law
Pengaruh Investasi Modal dan Upah Terhadap Penyerapan Tenaga Kerja pada Sektor Industri di Kota Makassar Periode 2003 - 2013
Get PDFPenelitian ini bertujuan untuk mengetahui pengaruh investasi modal dan upah terhadap penyerapan tenaga kerja pada sektor industri di Kota Makassar periode 2003-2013.
Teknik pengolahan data menggunakan regresi linear berganda dengan metode Kuadrat Terkecil Biasa atau Ordinary Least Square (OLS) melalui program SPSS 21. Penelitian ini menggunakan jenis penelitian kuantitatif dan diolah dengan kebutuhan model yang digunakan. Data ini adalah data sekunder yang berasal dari catatan atau laporan historis yang tersusun dalam arsip yang dipublikasikan maupun yang tidak dipublikasikan.
Hasil penelitian menunjukkan bahwa secara simultan variabel investasi dan upah berpengaruh signifikan terhadap penyerapan tenaga kerja pada sektor industri. Secara parsial, variable investasi berpengaruh signifikan terhadap penyerapan tenaga kerja pada sektor industri dengan nilai koefisien sebesar 1,757 begitu pula variable upah berpengaruh signifikan terhadap penyerapan tenaga kerja pada sektor industri dengan nilai koefisien sebesar -558. Dari hasi Regresi, nilaiR- Squared (R2) sebesar 0, 920. Hal ini berarti variable independen mampu menjelaskan variasi penyerapan tenaga kerja di Kota Makassar sebesar 92% sedangkan sisanya 8% dijelaskan oleh variabel-variabel lain di luar model.Faktor yang paling dominan adalah investasi dengan nilai signifikansi sebesar 0,000
PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS
Get PDFThe COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing the Nadam optimizer achieved an MAPE score of 9.76%. On the other hand, the CNN-biLSTM model utilizing the Nadam optimizer demonstrated a slightly improved MAPE score of 9.62%. For Australia, the biLSTM model using the Nadam optimizer obtained an MAPE score of 31.52%. However, the CNN-biLSTM model employing the RMSprop optimizer had a marginally higher MAPE score of 33.33%. In Chile, the biLSTM model using the Adam optimizer obtained an MAPE score of 44.04%. Conversely, the CNN-biLSTM model using the RMSprop optimizer had a slightly higher MAPE score of 44.09%. Lastly, in Canada, the biLSTM model using the Nadam optimizer achieved a comparatively low MAPE score of 14.99%. Similarly, the CNN-biLSTM model utilizing the Adam optimizer demonstrated a slightly better MAPE score of 14.75%. These results highlight that the choice of optimization technique, model architecture, and balanced dataset can significantly influence the prediction accuracy of airport traffic
Decision Support System for Supplier Selection using Analytical Hierarchy Process (AHP) Method
No full textTechnological growth is characterized by a variety of technological finds and computational methods used to facilitate human work in the era of globalization that is the method of Decision Support System (DSS) that helps decision makers to use data and models to solve problems that are not Structured. Toko Harapan Baru in making decisions in determining the best suppliers still use intuition, analysis, calculation and comparison of the manual in determining the supplier of goods to his shop takes a long time, the difficulty of searching data because there is no data processing cause is quite complicated without any particular method which gives inaccurate results. Then, Toko Harapan Baru requires a system that can be a solution to the problem that is being encountered. The Analytical Hierarchy Process (AHP) method is a functional hierarchy to help decision-makers better in making decisions on many objective issues.</jats:p
Performance Prediction of Airport Traffic Using LSTM and CNN-LSTM Models
Get PDFDuring the COVID-19 pandemic, airports faced a significant drop in passenger numbers, impacting the vital hub of the aircraft transportation industry. This study aimed to evaluate whether Long Short-Term Memory Network (LSTM) and Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) offer more accurate predictions for airport traffic during the COVID-19 pandemic from March to December 2020. The studies involved data filtering, applying min-max scaling, and dividing the dataset into 80% training and 20% testing sets. Parameter adjustment was performed with different optimizers such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. Performance evaluation uses metrics that include Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The best LSTM model achieved an impressive MAPE score of 0.0932, while the CNN-LSTM model had a slightly higher score of 0.0960. In particular, the inclusion of a balanced data set representing a percentage of the base period for each airport had a significant impact on improving prediction accuracy. This research contributes to providing stakeholders with valuable insights into the effectiveness of predicting airport traffic patterns during these unprecedented times
Comparative Analysis of Optimizer Effectiveness in GRU and CNN-GRU Models for Airport Traffic Prediction
Get PDFThe COVID-19 pandemic has posed significant challenges to airport traffic management, necessitating accurate predictive models. This research evaluates the effectiveness of various optimizers in enhancing airport traffic prediction using Deep Learning models, specifically Gated Recurrent Units (GRU) and Convolutional Neural Network-Gated Recurrent Units (CNN-GRU). We compare the performance of optimizers including RMSprop, Adam, Nadam, AdamW, Adamax, and Lion, and analyze the impact of their parameter tuning on model accuracy. Time series data from airports in the United States, Canada, Chile, and Australia were used, with preprocessing steps like filtering, cleaning, and applying a MinMax Scaler. The data was split into 80% for training and 20% for testing. Our findings reveal that the Adam optimizer paired with the GRU model achieved the lowest Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in the USA. The study underscores the importance of selecting and tuning optimizers, with ReduceLROnPlateau used to adjust the learning rate dynamically, preventing overfitting and improving model convergence. However, limitations include dataset imbalance and region-specific results, which may affect the generalizability of the findings. Future research should address these limitations by developing balanced datasets and exploring optimizer performance across a broader range of regions and conditions. This study lays the groundwork for further investigating sustainable and accurate airport traffic prediction models
Oikeusturvan toteutuminen poikkeusolojen aikana
Get PDFSuomen perustuslakiin kirjatuista perusoikeuksista yksi on oikeusturva, joka turvaa yksilölle oikeuden saada asiansa käsitellyksi sekä oikeuden saada perusteltu päätös (21 §). Oikeus-turva takaa yksilölle oikeudet omiin oikeuksiinsa ja täten myös turvaa muiden perusoi-keuksien toteutumista. Oikeusturva on myös merkittävä osa oikeusvaltioperiaatetta ja sen toteutumista. Perustuslaissa on kuitenkin annettu mahdollisuus tehdä poikkeuksia perus-oikeuksiin, mikäli erityinen tilanne tämän vaatii (23 §). Poikkeusolot on määritelty val-miuslaissa (3 §). Poikkeusoloja ovat sellaiset kriisitilanteet, jossa yhteiskunnan normaali-toiminta on merkittävästi uhattuna.
Tässä lainopillisessa tutkielmassa tarkastellaan sitä, miten oikeusturvaa toteutetaan myös poikkeusolojen aikana. Tutkielma keskittyy tarkastelemaan oikeusturvaa käsitteenä ja pe-rusoikeutena sekä oikeusvaltion elementtinä. Lisäksi tarkastellaan poikkeusolojen oikeu-dellista sääntelyä sekä oikeusturvan toteutumisen turvaamista poikkeusolojen aikana. Tut-kielman keskiössä on kansallinen lainsäädäntö, jota täydennetään EU-oikeudella.
Tutkimuksesta käy ilmi, että oikeusturvaa pidetään yhtenä tärkeimpänä oikeutena ja sen turvaaminen on otettu julkisessa toiminnassa huomioon. Oikeusvaltioperiaate sekä hallin-non oikeusperiaatteet suojaavat oikeusturvan toteutumista. Perustuslakivaliokunta, ylim-mät laillisuusvalvojat sekä tuomioistuimet toteuttavat oikeusturvaa omilla osa-alueillaan.
Tutkimuksessa kävi ilmi, että poikkeusoloissakin lähtökohtana on se, ettei ylimääräisiä ra-joituksia perusoikeuksiin tehtäisi eikä laajempia toimivaltuuksia otettaisi käyttöön, jos tilanne on mahdollista hoitaa normaalein toimivaltuuksin. Tätä tulosta puoltavat säädökset löytyvät valmiuslaista ja puolustustilalaista. Tutkielmassa tultiin myös siihen tulokseen, että ennakointi ja hyvä lainsäädännön valmistelu mahdollisia kriisitilanteita varten turvaa-vat perusoikeuksien paremman toteutumisen. Tutkimuksessa tultiin myös tulokseen, että valmiuslain jo itsessään on tarkoitus ylläpitää oikeusjärjestystä ja perusoikeuksia. Oikeus-turvan toteutuminen poikkeusoloissa on kiinni varautumisesta, oikeusvaltioperiaatteen noudattamisesta ja turvaamisesta
Gathering 4. Invisible Murmurs: Mapping Invisibility and Belonging in Contemporary British Art
No full textComparison of ANOVA and Chi-Square Feature Selection Methods to Improve Machine Learning Performance in Anemia Classification
Get PDFAnemia is a prevalent hematological condition marked by decreased hemoglobin concentration in the blood, which can lead to serious health complications if undetected. Although machine learning has shown potential in supporting early diagnosis, its effectiveness is often hindered by irrelevant or excessive features. This study investigates the impact of ANOVA and Chi-Square feature selection methods in improving the effectiveness of three distinct machine learning models algorithms, Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) for anemia classification. Using a Kaggle dataset consisting of 15,300 instances and 25 features, the evaluation of each model was conducted with reference to its accuracy, precision, recall, and F1-score, both before and after applying feature selection. Experimental results show a substantial improvement in classification performance after feature selection, with the SVM + ANOVA combination achieving the highest accuracy of 94.61%. In contrast, models without feature selection performed below 90%, highlighting the need for appropriate feature reduction techniques. This study contributes a comparative analysis framework for medical data classification, emphasizing the role of statistical feature selection in optimizing model accuracy. Its novelty lies in demonstrating consistent performance improvement across algorithms using real-world anemia data and providing evidence that ANOVA and Chi-Square can significantly enhance model generalization in medical diagnostic contexts
Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases
No full textHeart disease is a major problem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data has collaborated with information technology such as machine learning to detect, predict, and classify diseases. This research aims to improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with particle swarm optimization (PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improve the K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935
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